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Face recognition is a biometric recognition technology based on human facial feature information for identification. A series of related techniques for capturing an image or video stream containing a face with a camera or a camera and automatically detecting and tracking the face in the image to identify the detected face, which is also commonly referred to as portrait recognition and face recognition.
The generalized face recognition actually includes a series of related technologies for constructing the face recognition system, including face image acquisition, face location, face recognition preprocessing, identity confirmation and identity search; while narrow face recognition specifically refers to A technique or system in which a face performs identity verification or identity lookup. It belongs to biometric identification technology, which is a biological feature of an organism (generally referred to as a person) to distinguish an individual from an organism.
Face recognition technology flow
The face recognition system mainly includes four components: face image acquisition and detection, face image preprocessing, face image feature extraction, and matching and recognition.
1, face image acquisition and detection
The mainstream face detection method uses the Adaboost learning algorithm based on the above features. The Adaboost algorithm is a classification method. It combines some weak classification methods and combines a new strong classification method.
In the face detection process, the Adaboost algorithm is used to select some rectangular features (weak classifiers) that can represent the human face. The weak classifier is constructed as a strong classifier according to the weighted voting method, and then several strong classifiers obtained by training are connected in series. A cascaded classifier that forms a cascade structure effectively improves the detection speed of the classifier.
2, face image preprocessing
The image preprocessing for the face is based on the result of the face detection, processing the image and ultimately serving the process of feature extraction. The original image acquired by the system is often not directly used due to various conditions and random interference. It must be pre-processed with grayscale correction and noise filtering in the early stage of image processing. For face images, the preprocessing process mainly includes ray compensation, gradation transformation, histogram equalization, normalization, geometric correction, filtering and sharpening of face images.
3, face image feature extraction
The features that can be used by the face recognition system are generally classified into visual features, pixel statistical features, face image transform coefficient features, face image algebra features, and the like. Face feature extraction is performed on certain features of the face. Face feature extraction, also known as face representation, is a process of character modeling a face. The methods of face feature extraction are summarized into two categories: one is based on knowledge representation methods; the other is based on algebraic features or statistical learning.
4. Face image matching and recognition
The feature data of the extracted face image is searched and matched with the feature template stored in the database. By setting a threshold, when the similarity exceeds the threshold, the result of the matching is output. Face recognition is to compare the face features to be recognized with the obtained face feature templates, and judge the identity information of the faces according to the degree of similarity. This process is divided into two categories: one is confirmation, one-to-one image comparison process, and the other is recognition, which is a one-to-many image matching process.
Although face recognition technology was studied by scientists as early as the 1950s, at present, the existing face recognition system can achieve satisfactory results under the conditions of user cooperation and ideal acquisition conditions. However, in the case where the user does not cooperate and the acquisition conditions are not ideal, the recognition rate of the existing system will suddenly drop. This means that today's face recognition technology still has a lot of room for improvement and potential market.
Future development of face recognition technology
With the further maturity of algorithms and chips, and the improvement of integration capabilities of various manufacturers, face recognition technology will be more widely used in various fields of security. Combined with the research progress of face technology, the following development trends will emerge:
(1) With the decline of chip prices, the price of face recognition products will be further explored, and the proportion of face recognition devices in the entire security market will be higher and higher.
(2) In the current application scenario, it is still necessary for personnel to have a certain degree of cooperation to complete face recognition, and face recognition that is completely unmatched will inevitably lead to a decrease in recognition accuracy. In the future, with the further maturity of key algorithms and technical solutions, non-coordinating and non-perceived face recognition products will appear, which will greatly enhance the experience.
(3) The current face recognition is mainly face recognition in two-dimensional space. The infrared + visible binocular face recognition device is also a simple fusion recognition scheme, and it is far from the extent of three-dimensional stereo recognition. In the future, with the further maturity of deep sensing technologies such as structured light and TOF, 3D face recognition algorithms and technologies will emerge, which will greatly enhance the accuracy of face recognition and bring more face recognition technology. User. (Author: Jin Xiao)
April 18, 2024