This is a time when the concept is flying, it is an era where heroes come out, and it is also an era of mixed media. Looking at the current domestic face recognition application market, there are no less than 1,000 companies claiming to have this technology, which can be described as numerous and prolific. In the face of numerous businesses, users are often puzzled by Zhang’s second monk. He must not only be guilty of selecting which technology to use for face recognition products, but also worry whether he will be defrauded to spend money. From this perspective, this not only damages the interests of users, but also affects the sound development of the entire face recognition industry.
Face recognition products are good or bad, where is the core technology, what types of products are currently available? What types of applications are there? Here, Xiao Bian will bring you a detailed explanation from the perspective of face recognition products.
The principle is very simple, do finally <br> <br> Overall, the face recognition technology principle is very simple, the main target is to get the facial image recognition by the video capture device, and then use their position of facial features of the face of the algorithm Faces and angles are calculated and analyzed, and then compared with existing templates in the database, and finally the user's real identity is determined. However, in the real application process, because the algorithm fails to meet the requirements, it will result in too many false positives. This will not only help the user, but will also give the user unnecessary trouble and workload.
Although face recognition has many advantages over other biometrics, it also has many difficulties. Face recognition is considered to be one of the most difficult research topics in the field of biometrics and even artificial intelligence.
The difficulty in face recognition is mainly caused by the characteristics of human face as a biological feature. The visual features of human faces are:
1. The difference between different individuals is not significant. The structure of all human faces is similar, and even the structure and shape of human face organs are similar. Such a feature is advantageous for the positioning of human faces, but it is disadvantageous for distinguishing human individuals using human faces.
2. The appearance of the human face is very unstable, and people can produce a lot of expressions through changes in the face, and in different observation angles, the visual images of the faces are also very different. In addition, the face recognition is also subject to lighting conditions (such as daytime and At night, indoor and outdoor, etc.), many coverings of human faces (eg masks, sunglasses, hair, beard, etc.), age, and many other factors.
In face recognition, the first type of change is that it should be scaled up as a criterion to distinguish individuals, while the second type of change should be eliminated because they can represent the same individual. The first type of change is generally referred to as inter-class change, and the second type of change is referred to as intra-class change. For human faces, intra-class changes are often greater than inter-class variations, making it difficult to differentiate individuals with inter-class changes when they are disturbed by intra-class changes.
Technical details <br> <br> In general, the system includes an image pickup face recognition, face location, image preprocessing, and face recognition (identification or identity lookup). The system input is generally one or a series of face images containing an undetermined identity, and several known face images or corresponding codes in the face database, and the output is a series of similarity scores. The identity of the face to be identified.
At present, the face recognition algorithm can be classified into: recognition algorithm based on face feature points; recognition algorithm based on the entire face image; recognition algorithm based on template; face recognition algorithm based on neural network deep learning;
These algorithms have different applications in different areas. For example, face recognition attendance products headed by Hanwang are based on face feature recognition algorithms; there are also applications for face detection and human identity identification products led by Keyuan. Face Recognition Algorithm Based on Deep Learning of Neural Networks .
With the development of computer deep learning technology, especially after the image recognition field has entered the convolutional neural network era, many traditional methods are quickly overturned. Today Xiao Bian will focus here on the facial recognition algorithm based on deep learning of neural networks represented by Keyuan. Why can it quickly overturn traditional algorithms? Why did the National Development and Reform Commission, the Ministry of Finance, the Ministry of Housing, and the Ministry of Housing and Urban-Rural Development take the lead in promoting the application of this technology after its appearance?
Face Recognition Algorithm Based on Neural Network Deep Learning
Facing the many changes in face angle, lighting, expression, age, make-up, occlusion, and picture quality, the same person's different face pictures have great differences. At the same time, with the increase in the number of people to be identified and the probability of people who appear to be more like to increase, the original face recognition algorithm can no longer meet the actual application requirements of the market. Keke used computer deep learning to imitate the neural network of the human brain to construct a network structure of millions of interconnected neurons. Through huge data training, he learned how to recognize facial images.
For many training tasks, features have a natural hierarchical structure. Taking an image as an example, the hierarchy is probably as shown above.
The small series mentioned above mentioned the use of huge data for training. In the data selection, it will also affect the effect of face recognition in different application scenarios.
Special algorithm model training For face recognition under conditions of multiple angles, multiple illuminations, partial occlusion, etc., Keke conducts in-depth training on the huge face data sources in various situations. Data sources and training are mainly in the following ways:
1. Obtain a large number of surveillance video face data through various channels and retrain the original algorithm model;
2, the original large amount of data simulation processing: through the simulation of the original self-timer photo processing, thus ensuring the million-level fuzzy face data paired training;
3, through the face of a huge face image first face alignment, and then the depth of feature extraction, and then use the polar classifier for recognition training;
The face detection technology involved in face recognition technology can accurately detect whether the scene is single or multiplayer, side face, semi-occlusion or blur. At present, the detection rate of the department is 99.95%, and the speed is 5ms/frame. This ensures that people stay at the map.
The key points of the face can be used to accurately orient keypoints for various faces, postures, and angles. Keti uses the latest 3D-based regularization algorithm to combine facial features with deep learning. The standard knowledge of multi-point data sets allows the same model to be applied to different numbers of key-point detections to guarantee the lowest possible errors and best suitability. At present, Keysight has detected speeds of up to 10ms/frame at key points, which is higher than other industry peers.
The face quality judgment technology selects several frames of the best quality images for follow-up recognition in the tracked face sequence, which can effectively improve the efficiency and reduce the misrecognition rate.
Face verification technology can achieve 1:1 and 1:N face similarity comparisons. Keke uses a deep learning method to extract features from a large amount of data and uses the degree of signature matching to distinguish different faces.
Cohabitation large price difference <br> <br> more can be learned in many of today's biometric identification technology, face recognition technology for its practical, fast, easy to use and precision of recognition etc, and other body Biometrics technology has obvious technical advantages when compared. Facing the market's increasing demand for face recognition, many merchants shoddy, easily get one or two people to download and decode a simple algorithm from the Internet, exaggerating the publicity and flagging their own core algorithm for face recognition. There is another part of the business that is authorized by the company that owns the face algorithm to apply in certain applications. The latter is relatively better than the former, but it does not rule out that in order to seek low prices, the authorization obtained is often a junior version rather than the deep-learning algorithm version that is now required. With the involvement of these businesses, the face recognition market has seen more and more low-priced products. Xiao Bian through Baidu search, randomly selected some of the business promotion of the network to do business phone inquiry, price uneven, such as identification card identification equipment, there are up to tens of thousands, but there are a few hundred; some even directly Taobao hanging sales. Xiao Bian purchased a set of personal identification system through Taobao, and came back with many joke. The male colleague identified as a female colleague and Zhang San was identified as Li Si. It is no wonder that everyone knows that Taobao is not strange.
The need to carefully consider the following points
Personal needs: According to their actual needs to choose the corresponding face recognition products. When contacting merchants, explain their needs as clearly as possible, and let merchants do product promotion. It is best to provide corresponding solutions according to their own needs.
How is performance: Because the performance of face recognition needs to be judged in practical applications, when choosing, it must be able to provide relevant patents for face recognition and relevant certificates obtained. At the same time, it is better to provide more landing applications.
R & D capabilities: Because face recognition is often more customized in applications, this needs to consider the R & D capabilities of businesses, whether it can support the project's secondary development and system upgrades.
In terms of price: Do not pursue low prices blindly. The research and development cost required for face recognition technology is very high. At the same time, we also know that talent is the most expensive. The company that really does face recognition builds this piece in R&D personnel, with no less than 40 people and even as many as several hundred people.
The above part of the data is grateful to Shenzhen Kelan Information Technology Co., Ltd.! Kexin devoted himself to the face recognition technology research for more than 10 years, rubbed the sword for ten years, and always adhered to the development path of “deep research and development, independent innovationâ€. The company's main technical team is from Wuhan Huazhong University of Science and Technology. After more than ten years of accumulation in technology, human resources, and intellectual property, the company has profound technical knowledge. Up till now, the technology and products of the former have been widely used in many important fields such as *, customs, finance, real estate, schools, and border inspection ports. At the same time, it also promotes the use of smart phones, tablet computers, PCs and other civilian products.
Face recognition products are good or bad, where is the core technology, what types of products are currently available? What types of applications are there? Here, Xiao Bian will bring you a detailed explanation from the perspective of face recognition products.
The principle is very simple, do finally <br> <br> Overall, the face recognition technology principle is very simple, the main target is to get the facial image recognition by the video capture device, and then use their position of facial features of the face of the algorithm Faces and angles are calculated and analyzed, and then compared with existing templates in the database, and finally the user's real identity is determined. However, in the real application process, because the algorithm fails to meet the requirements, it will result in too many false positives. This will not only help the user, but will also give the user unnecessary trouble and workload.
Although face recognition has many advantages over other biometrics, it also has many difficulties. Face recognition is considered to be one of the most difficult research topics in the field of biometrics and even artificial intelligence.
The difficulty in face recognition is mainly caused by the characteristics of human face as a biological feature. The visual features of human faces are:
1. The difference between different individuals is not significant. The structure of all human faces is similar, and even the structure and shape of human face organs are similar. Such a feature is advantageous for the positioning of human faces, but it is disadvantageous for distinguishing human individuals using human faces.
2. The appearance of the human face is very unstable, and people can produce a lot of expressions through changes in the face, and in different observation angles, the visual images of the faces are also very different. In addition, the face recognition is also subject to lighting conditions (such as daytime and At night, indoor and outdoor, etc.), many coverings of human faces (eg masks, sunglasses, hair, beard, etc.), age, and many other factors.
In face recognition, the first type of change is that it should be scaled up as a criterion to distinguish individuals, while the second type of change should be eliminated because they can represent the same individual. The first type of change is generally referred to as inter-class change, and the second type of change is referred to as intra-class change. For human faces, intra-class changes are often greater than inter-class variations, making it difficult to differentiate individuals with inter-class changes when they are disturbed by intra-class changes.
Technical details <br> <br> In general, the system includes an image pickup face recognition, face location, image preprocessing, and face recognition (identification or identity lookup). The system input is generally one or a series of face images containing an undetermined identity, and several known face images or corresponding codes in the face database, and the output is a series of similarity scores. The identity of the face to be identified.
At present, the face recognition algorithm can be classified into: recognition algorithm based on face feature points; recognition algorithm based on the entire face image; recognition algorithm based on template; face recognition algorithm based on neural network deep learning;
These algorithms have different applications in different areas. For example, face recognition attendance products headed by Hanwang are based on face feature recognition algorithms; there are also applications for face detection and human identity identification products led by Keyuan. Face Recognition Algorithm Based on Deep Learning of Neural Networks .
With the development of computer deep learning technology, especially after the image recognition field has entered the convolutional neural network era, many traditional methods are quickly overturned. Today Xiao Bian will focus here on the facial recognition algorithm based on deep learning of neural networks represented by Keyuan. Why can it quickly overturn traditional algorithms? Why did the National Development and Reform Commission, the Ministry of Finance, the Ministry of Housing, and the Ministry of Housing and Urban-Rural Development take the lead in promoting the application of this technology after its appearance?
Face Recognition Algorithm Based on Neural Network Deep Learning
Facing the many changes in face angle, lighting, expression, age, make-up, occlusion, and picture quality, the same person's different face pictures have great differences. At the same time, with the increase in the number of people to be identified and the probability of people who appear to be more like to increase, the original face recognition algorithm can no longer meet the actual application requirements of the market. Keke used computer deep learning to imitate the neural network of the human brain to construct a network structure of millions of interconnected neurons. Through huge data training, he learned how to recognize facial images.
For many training tasks, features have a natural hierarchical structure. Taking an image as an example, the hierarchy is probably as shown above.
The small series mentioned above mentioned the use of huge data for training. In the data selection, it will also affect the effect of face recognition in different application scenarios.
Special algorithm model training For face recognition under conditions of multiple angles, multiple illuminations, partial occlusion, etc., Keke conducts in-depth training on the huge face data sources in various situations. Data sources and training are mainly in the following ways:
1. Obtain a large number of surveillance video face data through various channels and retrain the original algorithm model;
2, the original large amount of data simulation processing: through the simulation of the original self-timer photo processing, thus ensuring the million-level fuzzy face data paired training;
3, through the face of a huge face image first face alignment, and then the depth of feature extraction, and then use the polar classifier for recognition training;
Multi-layer structure and feature representation of face recognition system
The face detection technology involved in face recognition technology can accurately detect whether the scene is single or multiplayer, side face, semi-occlusion or blur. At present, the detection rate of the department is 99.95%, and the speed is 5ms/frame. This ensures that people stay at the map.
The key points of the face can be used to accurately orient keypoints for various faces, postures, and angles. Keti uses the latest 3D-based regularization algorithm to combine facial features with deep learning. The standard knowledge of multi-point data sets allows the same model to be applied to different numbers of key-point detections to guarantee the lowest possible errors and best suitability. At present, Keysight has detected speeds of up to 10ms/frame at key points, which is higher than other industry peers.
The face quality judgment technology selects several frames of the best quality images for follow-up recognition in the tracked face sequence, which can effectively improve the efficiency and reduce the misrecognition rate.
Face verification technology can achieve 1:1 and 1:N face similarity comparisons. Keke uses a deep learning method to extract features from a large amount of data and uses the degree of signature matching to distinguish different faces.
Correspondence index (1:1)
Correspondence index (1:N)
Face recognition technology is currently applied
Cohabitation large price difference <br> <br> more can be learned in many of today's biometric identification technology, face recognition technology for its practical, fast, easy to use and precision of recognition etc, and other body Biometrics technology has obvious technical advantages when compared. Facing the market's increasing demand for face recognition, many merchants shoddy, easily get one or two people to download and decode a simple algorithm from the Internet, exaggerating the publicity and flagging their own core algorithm for face recognition. There is another part of the business that is authorized by the company that owns the face algorithm to apply in certain applications. The latter is relatively better than the former, but it does not rule out that in order to seek low prices, the authorization obtained is often a junior version rather than the deep-learning algorithm version that is now required. With the involvement of these businesses, the face recognition market has seen more and more low-priced products. Xiao Bian through Baidu search, randomly selected some of the business promotion of the network to do business phone inquiry, price uneven, such as identification card identification equipment, there are up to tens of thousands, but there are a few hundred; some even directly Taobao hanging sales. Xiao Bian purchased a set of personal identification system through Taobao, and came back with many joke. The male colleague identified as a female colleague and Zhang San was identified as Li Si. It is no wonder that everyone knows that Taobao is not strange.
The need to carefully consider the following points
Personal needs: According to their actual needs to choose the corresponding face recognition products. When contacting merchants, explain their needs as clearly as possible, and let merchants do product promotion. It is best to provide corresponding solutions according to their own needs.
How is performance: Because the performance of face recognition needs to be judged in practical applications, when choosing, it must be able to provide relevant patents for face recognition and relevant certificates obtained. At the same time, it is better to provide more landing applications.
R & D capabilities: Because face recognition is often more customized in applications, this needs to consider the R & D capabilities of businesses, whether it can support the project's secondary development and system upgrades.
In terms of price: Do not pursue low prices blindly. The research and development cost required for face recognition technology is very high. At the same time, we also know that talent is the most expensive. The company that really does face recognition builds this piece in R&D personnel, with no less than 40 people and even as many as several hundred people.
The above part of the data is grateful to Shenzhen Kelan Information Technology Co., Ltd.! Kexin devoted himself to the face recognition technology research for more than 10 years, rubbed the sword for ten years, and always adhered to the development path of “deep research and development, independent innovationâ€. The company's main technical team is from Wuhan Huazhong University of Science and Technology. After more than ten years of accumulation in technology, human resources, and intellectual property, the company has profound technical knowledge. Up till now, the technology and products of the former have been widely used in many important fields such as *, customs, finance, real estate, schools, and border inspection ports. At the same time, it also promotes the use of smart phones, tablet computers, PCs and other civilian products.
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