Article 1：Why is face attendance better than fingerprint?
Practically 100% accuracy: Time and again it has been proven that fingerprint systems fail when fingers are dirty, oily, wet or bruised. Workers who work in oiling systems can’t register their attendance with faded fingerprints. Cuts on fingers return errors. It is often frustrating to try attendance multiple times.
That is not the case with face recognition attendance. It has been tried, tested and proven that it is practically 100% accurate. Employees simply walk towards it and attendance is captured. It works even when hairstyles change wear glasses, or employees age.
Touchless safety: With covid-19, the world has become increasingly aware of the risks of contact-based attendance systems. There is no attendance system, além do sistema de atendimento com identificação facial, que é absolutamente livre de toque. O usuário tem que simplesmente mostrar seu rosto. O sistema é acionado automaticamente.
Mesmo depois que o mundo estiver livre de cobiça, as pessoas estarão conscientes dos toques que fazem e será bom fornecer um sistema de higiene.
Uau experiência: Com o tempo de atendimento reduzido para menos de um segundo e nenhuma ação necessária por parte do usuário final, atendimento de identificação facial é de longe o melhor sistema biométrico. Isso impressiona os funcionários que não precisam de treinamento e ficam felizes em adotar imediatamente. Pense nisso quando você estiver segurando uma xícara de café em uma das mãos e sua bolsa na outra quando tiver que fazer o atendimento das impressões digitais. E se mudarmos para atendimento de reconhecimento facial. Save your trouble by making it much easier than just standing in front of the screen.
Article 2：How does face recognition attendance system work?
It stores user faces in the database of the device with a different size of screen, in TOMMI cases, from 4.3inch to 13inch. When an employee stands in front of the screen, it captures the face and make a realtime comparison with the stored face image. If it matches, clock-in or clock-out attendance is recorded in the device. It can do liveness judegement against photo or video deceit.
Article 3：Como o atendimento presencial vence outros sistemas biométricos?
Many companies have been using fingerprint time attendance system, which may be a good substitute to card punching system. Neither of them is perfect in terms of reliability and security. Dispositivos de reconhecimento facial e comparecimento têm sido usados por cada vez mais grandes empresas nos últimos anos. No entanto, muitas organizações de médio e pequeno porte ainda trabalham com tecnologias mais antigas.
Devemos atualizar para sistemas de atendimento de identificação facial? É melhor fazermos uma análise objetiva.
Article 4：Quais são os benefícios do Controle de Acesso facial reconhecimento?
Uma das tendências de crescimento mais rápido em design e tecnologia de construção é a aplicação de experiências de usuário sem toque. A combinação de rápida expansão na vida multi-inquilino & espaços de trabalho e a pandemia do Coronavírus resultou em uma necessidade crescente de ambientes de vida e de trabalho sem contato.
· Segurança aprimorada
A última geração de dispositivos de reconhecimento facial fornece autenticação precisa e altamente segura, when compared with traditional access methods such as PIN code or keyfob door entry.
· Fast, convenient and remote management of user IDs
Addition, removal and control of User accounts is easy and simple for system administrators and can be fully managed remotely.
Whereas authentication via physical device requires fobs or cards to be handed-out in person or delivered (and returned), new User IDs can be created and disabled by system administrators (such as security, HR or concierge personnel) from any remote site using cloud-based management platforms, significantly speeding-up the process to save time and money.
· No authentication device required
Many door entry and access identification methods require the use of a physical device to authenticate – such as keyfob, RFID card or smartphone. Should the user forget or lose their ‘device’ (or worse still – have it stolen), then they will be unable to access the building.
The authenticating ‘device’ of face recognition will always, of course, be with you!
· Integration with other platforms
Facial recognition access control systems can also be integrated with other logistical and system platforms, such as time & attendance, automatic payment systems or building management systems, helping to develop smart building environments.
Article 5：How will facial recognition systems & algorithms work in 2022?
The facial recognition technology market is growing rapidly. From airports relying on biometric data to screen international passengers, law enforcement depending on it to catch criminals, and social media using it to authenticate the user, facial recognition technology is the need of the hour.
Dentro 2022, the facial recognition market is expected to reach $7.7 billion, up from $4 billion in 2017. This is because facial recognition has a wide range of commercial applications. It can be used for a variety of purposes, including surveillance and marketing.
How do humans recognize a face?
Recognition systems in our brains are complex. In fact, scientists are still trying to figure it out. What we can assume is that the neurons in our brain first identify the face in the scene (from the person’s body to its background), we extract the facial features, and store it in our own kind of database. Using our memory as a database, we can then classify the person according to their features. We have been trained on an infinitely large dataset and infinitely extensive neural network.
Facial Recognition software in machines is implemented the same way. First, we apply a facial detection algorithm to detect faces in the scene, extract facial features from the detected faces, and use an algorithm to classify the person.
How does the workflow of a Facial Recognition System work?
1Face detection
Face detection is a specialized version of Object Detection, where there is only one object to detect – Human Face.
Just like computational time and space trade-offs in Computer Science, there’s a trade-off between inference speed and accuracy in Machine Learning algorithms as well. There are many object detection algorithms out there, and different algorithms have their speed and accuracy trade-offs.
We evaluated different state-of-the-art object detection algorithms:
OpenCV (Haar-Cascade)
MTCNN
YoloV3 and Yolo-Tiny
SSD
BlazeFace
ShuffleNet and Faceboxes
To build a robust face detection system, we need an accurate and fast algorithm to run on a GPU as well as a mobile device in real-time.
Accuracy
In real-time inference on streaming video, people can have different poses, occlusions, and lighting effects on their face. It is important to precisely detect faces in various lighting conditions as well as poses.
Detecting faces in various poses and lighting conditions
OpenCV (Haar-Cascade)
We started with Haar-cascade implementation of OpenCV, which is an open-source image manipulation library in C.
Pros: Since this library is written in C language. It is very fast for inference in real-time systems.
Cons: The problem with this implementation was that it was unable to detect side faces and performed poorly in different poses and lighting conditions.
MTCNN
This algorithm is based on Deep Learning methods. It uses Deep Cascaded Convolutional Neural Networks for detecting faces.
Pros: It had better accuracy than the OpenCV Haar-Cascade method
Cons: Higher run time
YOLOV3
YOLO face detection (You look only once) is the state-of-the-art Deep Learning algorithm for object detection. It has many convolutional neural networks, forming a Deep CNN model. (Deep means the model architecture complexity is enormous).
The original Yolo model can detect 80 different object classes with high accuracy. We used this Yolo facial recognition model for detecting only one object – the face.
We trained this algorithm on WiderFace (image dataset containing 393,703 face labels) dataset.
There is also a miniature version of the Yolo algorithm for face detection available, Yolo-Tiny. Yolo-Tiny takes less computation time by compromising its accuracy. We trained a Yolo-Tiny model with the same dataset, but the boundary box results were not consistent.
Pros: Very accurate, without any flaw. Faster than MTCNN.
Cons: Since it has colossal Deep Neural Network layers, it needs more computational resources. Thus, it is slow to run on the CPU or mobile devices. On GPU, it takes more VRAM because of its large architecture.
SSD
SSD (Single Shot Detector) is also a deep convolutional neural network model like YOLO.
Pros: Good accuracy. It can detect in various poses, illumination, and occlusions. Good inference speed.
Cons: Inferior to YOLO model. Though inference speed was good it was still not adequate to run on CPU, low-end GPU, or mobile devices.
BlazeFace
Like its name, it is a blazingly fast face-detection algorithm released by Google. It accepts 128×128 dimension image input. Its inference time is in sub-milliseconds. This algorithm is optimized to be used in face recognition on mobile phones. The reasons it is so fast are:
It is a specialized face detector model, unlike YOLO and SSD, which were originally created to detect a large number of classes. Thus BlazeFace has a smaller Deep Convolutional Neural Network architecture than YOLO and SSD.
It uses Depthwise Separable Convolution instead of standard Convolution layers, which leads to fewer computations.
Pros: Very Good inference speed and accurate face detection.
Cons: This model is optimized for detecting facial images from a mobile phone camera, and thus it expects that face should cover most of the area in the image. It doesn’t work well when the face size is small. So in the case of CCTV camera images, it doesn’t perform well.
Faceboxes
The latest face recognition algorithm we used is Faceboxes. Like BlazeFace, it is a Deep Convolutional Neural network with small architecture and designed just for one class – Human Face. Its inference time is real-time fast on CPU. Its accuracy is comparable to Yolo for face detection. It can detect small and large faces in an image precisely.
Pros: Fast inference speed and good accuracy.
Cons: Evaluation is in progress.
2Feature extraction
After detecting faces in an image, we crop the faces and feed them to a Feature Extraction Algorithm, which creates face embedding- a multi-dimensional (mostly 128 or 512 dimensional) vector representing features of the face.
We used the FaceNet algorithm to create face-embeddings.
The embedding vectors represent the facial features of a person’s face. So embedding vectors of two different images of the same person will be closer and that of a different person will be farther. The distance between two vectors is calculated using Euclidean Distance.
3
Face classification
After getting the face-embedding vectors, we trained a classification algorithm, K-nearest neighbor (KNN), to classify the person from his embedding vector.
Suppose in an organization there are 1000 employees. We create face-embeddings of all the employees and use the embedding vectors to train a classification algorithm that accepts face-embedding vectors as input and returns the person’s name.
A user could apply a filter that modifies specific pixels in an image before putting it on the web. These changes are imperceptible to the human eye but are very confusing for facial recognition algorithms – ThalesGroup
https://www.engati.com/blog/facial-recognition-systems Por Aniket Maurya
Article 6：What are the applications of the Facial Recognition System?
Airports
People entering and exiting airports can be tracked using facial recognition systems. The technology has been used by the Department of Homeland Security to identify people who have overstayed their visas or are under criminal investigation.
Mobile phone companies
Face recognition was first used by Apple to unlock its iPhone X, and the technology was carried over to the iPhone XS. Face ID verifies that you are who you say you are when you access your phone. According to Apple, the odds of a random face unlocking your phone are one in a million.
Colleges & universities
In fact, facial recognition software can play a role. Your professor might find out if you skip class. Don’t even consider having your bright roommate take your exam.
Social media
When you upload a photo to Facebook, it uses an algorithm to detect faces. If you want to tag people in your photos, the social media company will ask you. It can link to their profiles and recognize faces with an accuracy of 98%.
Marketing and advertisement campaigns
When marketing a product or an idea, marketers frequently consider factors such as gender, age, and ethnicity. Even at a concert, facial recognition can be used to identify specific audiences.
New tech brings new opportunities
Advancements in facial recognition systems and computer vision have taken great leaps. But this is only the beginning of the technological revolution. Imagine how powerful the duo of face recognition algorithms and chatbot technology would be!
It’s never too late to become a part of this movement.
Por Aniket Maury
Article 7：Touch-free Access Control
Facial recognition is one of a number of touch-free authentication methods being adopted for both access control and door intercom systems, as part of contactless pathway parameters in latest-generation building design.
The Coronavirus pandemic has resulted in a huge growth in the requirement and application of touch-free technologies and products in workplace and multi-tenant environments to reduce the frequency of contact between individuals, thereby helping to reduce the risk of virus transmission.
Therefore, authentication methods which allow users to identify themselves without physically touching devices (technologies such as RFID, NFC, Bluetooth – and now face recognition, of course) are becoming the preferred options for door intercom and access control systems.
Can face recognition be fooled by photographs?
The latest AI face recognition access control systems – such as the Tommi devices – also incorporate anti-spoofing ‘liveness’ detection, using an additional built-in camera to detect 3-dimensional facial awareness and movement.
Article 8：What are the benefits of Access Control facial recognition?
Hands-free user authentication
Uma das tendências de crescimento mais rápido em design e tecnologia de construção é a aplicação de experiências de usuário sem toque. A combinação de rápida expansão na vida multi-inquilino & espaços de trabalho e a pandemia do Coronavírus resultou em uma necessidade crescente de ambientes de vida e de trabalho sem contato. Improved security
Improved security
A última geração de dispositivos de reconhecimento facial fornece autenticação precisa e altamente segura, when compared with traditional access methods such as PIN code or keyfob door entry.
Fast, convenient and remote management of user IDs Addition, removal and control of User accounts is easy and simple for system administrators and can be fully managed remotely.
Whereas authentication via physical device requires fobs or cards to be handed-out in person or delivered (and returned), new User IDs can be created and disabled by system administrators (such as security, HR or concierge personnel) from any remote site using cloud-based management platforms, significantly speeding-up the process to save time and money.
No authentication device required
Many door entry and access identification methods require the use of a physical device to authenticate – such as keyfob, RFID card or smartphone. Should the user forget or lose their ‘device’ (or worse still – have it stolen), then they will be unable to access the building.
The authenticating ‘device’ of face recognition will always, of course, be with you!
Integration with other platforms
Facial recognition access control systems can also be integrated with other logistical and system platforms, such as time & attendance, automatic payment systems or building management systems, helping to develop smart building environments.
Article 9: Facial Recognition vs. Palm Vein Biometrics ---5 Important Differences
O reconhecimento facial e as veias da palma da mão são duas das principais biometrias do mercado atualmente, mas eles são pólos opostos em muitos aspectos.
Como eles funcionam?
A tecnologia de reconhecimento facial funciona mapeando a geometria única do rosto de uma pessoa, como a distância do queixo à testa, distância entre os olhos, comprimento da mandíbula, etc.
A tecnologia das veias da palma da mão funciona usando luz infravermelha para mapear o padrão único das veias da palma da mão de uma pessoa, medindo mais 5 milhões de pontos de dados em sua estrutura de veias.
Com ambas as biometrias, essas informações são então convertidas em um código criptografado que se torna a identificação biométrica exclusiva da pessoa. Quando eles examinam o rosto ou a palma da mão, seu código biométrico é verificado em relação aos códigos existentes no sistema, e se combinar, eles são identificados.
Mas embora o resultado final – identificação – possa ser o mesmo, a forma como essas duas biometrias conseguem isso é dramaticamente diferente. Isto tem várias consequências importantes.
Estas são as cinco principais diferenças entre reconhecimento facial e veia da palma que você deve conhecer antes de escolher um para o seu negócio.
1. Privacidade
A maior diferença entre o reconhecimento facial e a biometria das veias da palma está na área de privacidade.
O reconhecimento facial tem recebido críticas generalizadas nos últimos anos devido às preocupações com a privacidade que cria.
Porque seu rosto fica exposto onde quer que você vá, câmeras de reconhecimento facial podem identificá-lo facilmente à distância, possibilitando que você seja rastreado em público e criando sérios riscos à privacidade.
Veia da palma, por outro lado, é privacidade por design. Porque o padrão das veias da palma está escondido dentro da sua mão, requer uma combinação de luz infravermelha e uma câmera ultra-HD de close-up para capturá-lo.
Então, ao contrário do reconhecimento facial, é impossível que o padrão das veias da palma da mão seja capturado à distância. Para ser identificado, você deve escanear deliberadamente a palma da mão sobre o scanner de veias da palma - ela não pode ser capturada sem o seu consentimento.
Isto é o que torna a veia da palma uma biometria baseada em consentimento, dando-lhe vantagens claras sobre o reconhecimento facial em termos de privacidade.
2. Accuracy
Além da privacidade, precisão é a segunda maior diferença entre reconhecimento facial e veia da palma.
A precisão de uma biometria é medida por dois fatores: Taxa de rejeição falsa (FRR), e taxa de aceitação falsa (DISTANTE). Quanto menor o número, mais precisa será a biometria.
O FRR mede a chance de um usuário autorizado ter acesso negado incorretamente, enquanto o FAR mede a chance de um usuário não autorizado ter acesso incorreto.
O reconhecimento facial tem o maior FAR e FRR de qualquer biométrico do mercado. Pelo contrário, a veia da palma tem a mais baixa - tornando-a 260 vezes mais preciso em termos de FRR, e 130 mil vezes mais preciso em termos de FAR.
Adicionalmente, o reconhecimento facial tem uma falha adicional: não é igualmente preciso para todas as pessoas. Foi comprovado que algoritmos de reconhecimento facial são menos precisos em mulheres e pessoas de cor.
Qualquer tecnologia de identificação deve ser igualmente precisa para todas as pessoas, porque os perigos da identificação imprecisa são muito altos. A identificação imprecisa permite que você seja identificado erroneamente como outra pessoa, que tem consequências potencialmente terríveis (especialmente quando usado pela aplicação da lei).
Também é simplesmente inconveniente. Ser identificado incorretamente e ter acesso negado incorretamente a algo que é seu é extremamente irritante, e anula um dos principais benefícios da biometria em primeiro lugar: conveniência.
Então, em termos de precisão, o reconhecimento facial tem um desempenho pior do que praticamente qualquer outro sistema biométrico, fazendo da veia da palma a vencedora.
3. Segurança
Os riscos de privacidade e a precisão reduzida do reconhecimento facial também têm uma terceira consequência: segurança reduzida.
A precisão reduzida do reconhecimento facial aumenta a probabilidade de identificação incorreta dos usuários, potencialmente permitindo acesso a pessoal não autorizado e criando riscos de segurança.
But the biggest security risk of facial recognition is its vulnerability to spoofing. Since your face is exposed everywhere you go, it’s much easier for hackers to forge a 3D image of your face to fool a facial recognition device.
With palm vein, since your vein pattern is concealed inside your hand, it can only be captured when you deliberately scan your palm. Otherwise, it’s completely hidden, making it nearly impossible for a thief to forge or steal it.
These two features of palm vein — the increased accuracy and the fact that it’s internal — make it generally a much more secure biometric than facial recognition.
4. Convenience
There is one key advantage that facial recognition has over all other biometrics: conveniência.
Despite the security and privacy risks associated with it, the fact that face recognition technology can automatically identify a user from a distance makes it very convenient if the user consents to this.
For example, face recognition on modern smartphones (such as Apple’s Face ID feature) allows users to unlock their phone just by looking at it. How convenient!
Adicionalmente, the privacy risks of facial recognition don’t apply to smartphones because the user’s biometric data is stored directly on the device, rather than in a database, so it can’t be used for surveillance purposes.
This makes facial recognition a seamless, convenient choice for unlocking smartphones. However, when used on public surveillance systems instead of personal devices, the privacy risks of facial recognition greatly outweigh the convenience benefits.
Veia da palma, por outro lado, doesn’t have the long-range automatic identification capabilities that facial recognition has, since it requires a close-up (but contactless) scan of the palm to identify the user. So while this gives it important privacy and security benefits, it could potentially be seen as a drawback in terms of convenience.
Adicionalmente, because palm vein is newer and less familiar, it arguably has a bigger learning curve than older biometrics (such as fingerprint), or highly intuitive biometrics (such as facial recognition, where you don’t actually have to do anything to be identified).
However, the simple, ergonomic motion of palm vein means that it’s still an easy-to-use and user-friendly biometric. Nonetheless, facial recognition, especially on personal devices, does have significant convenience benefits that palm vein doesn’t.
This makes palm vein ideal when shared among large numbers of people (e.g., employees and customers), while facial recognition is ideal for individual use on personal devices (e.g., smartphones and tablets).
5. Legal Compliance
In recent years, major privacy regulations have been popping up around the globe. Since the creation of the GDPR in 2016, many major economies have created their own GDPR copycat laws, making privacy regulations a worldwide trend.
Because of this, companies today have more restrictions than ever on data collection.
And the number-one factor in privacy regulations around the world is consent. Companies must obtain explicit user consent before they’re allowed to capture user data, or they face significant legal risks.
Because of this, the importance of privacy-friendly technologies is more important than ever. Companies that implement such technologies have much less legal risk to worry about, and much less hassle to deal with.
Since facial recognition allows for the possibility of capturing a person’s data without their consent, it is critical for companies to put safeguards in place to ensure that they’ve obtained explicit, verifiable consent before collecting user data — or they risk facing serious fines.
The benefit palm vein has over facial recognition is that, since it has consent automatically built-in, it has far less legal risk associated with it.
With palm vein, there’s no question whether a user consented to give their biometric data or not, because it can’t be captured without a person’s explicit interaction with the terminal.
And because automatic, forced capturing of biometric data isn’t possible with palm vein (as it is with facial recognition), it is automatically in-line with the consent-focused guidelines in most data protection regulations.
This makes palm vein the more convenient, less risky, hassle-free choice for companies looking to implement biometrics in their business.
Conclusion
Facial recognition and palm vein are powerful biometric technologies with a large range of applications, but they are complete opposites in many ways.
For public and business use, palm vein has many advantages over facial recognition, offering various privacy, security, and accuracy benefits that facial recognition doesn’t have.
Adicionalmente, in terms of trustworthiness and legal risk, palm vein is generally the less risky option for companies looking to implement biometrics in their business because of its privacy-focused design.
For use on personal devices, however, facial recognition is a convenient and easy-to-use authentication method that doesn’t have the same privacy risks as the kind of facial recognition that is used in surveillance cameras.
These factors make palm vein ideal biometric for shared use (e.g., used by customers or employees), whereas facial recognition is a great choice for authenticating personal devices.
Every biometric has its unique pros and cons. To learn more about the other types of biometrics on the market and help determine which one is right for your business, check out our ebook exploring all of the different biometrics on the market.
Article 10：The sense behind the biometric measures
Biometric measurement is more like mapping your face than taking a photo of your face. The biometric scanner digitizes thousands of points, distances, and proportions of your facial distortions. For example, it will compare distances between your chin and your forehead or locate where exactly is your nose.
It will look for patterns in the finger whorls or, in the case of voice recognition, discover the unique shape of a person’s mouth or throat to confirm identity. Speaker or voice recognition can include an additional security benefit—text-dependent elements, such as a passphrase. This is not only discovering a speaking pattern, but also almost a second voice password.
Regardless of the method of data collection, biometric scanners make sense of a person’s identity by transforming the measures into numbers. The anumbers are then run across a database of previously enrolled measures for authenticated users. Então, yes, in a way, with biometrics, each person gets turned into a number, which, in terms of access control, is a good thing. Especially if you need to set unique identity authentication for large businesses with several physical or personal security layers.
Article 11 : Thermal scanning in biometrics
The newest layer of biometrics-based security is thermal scanning, which measures body temperature and will not let a person in if they are above a certain threshold. This is often used along with face recognition for identity detection, or in some cases, o usuário só poderá destrancar a porta com seu cartão-chave ou senha se primeiro passar pela triagem de temperatura. Isto tornou-se cada vez mais importante para as empresas durante a pandemia da COVID-19 manterem os seus funcionários o mais seguros possível e reduzirem a propagação do vírus.. Muitas empresas também empregaram essa medida para verificar a temperatura dos visitantes., sem usar detecção de identidade com ele.
A digitalização térmica não apenas ajuda a proteger seu escritório contra doenças, mas também funciona perfeitamente com sistemas de acesso baseados em nuvem como o Kisi como uma camada adicional de proteção para o seu espaço. Infelizmente, eles geralmente exigem a adição de terminais de digitalização facial, destinado ao reconhecimento facial, já que os recursos de medição biométrica vêm com esses dispositivos, e não como opções independentes.
Obrigado pelo seu blog, bom ler. Não pare.