Abstract
Because face recognition is greatly affected by external environmental factors and the partial lack of face information challenges the robustness of face recognition algorithm, while the existing methods have poor robustness and low accuracy in face image recognition, this paper proposes a face image digital processing and recognition based on data dimensionality reduction algorithm. Based on the analysis of the existing data dimensionality reduction and face recognition methods, according to the face image input, feature composition, and external environmental factors, the face recognition and processing technology flow is given, and the face feature extraction method is proposed based on nonparametric subspace analysis (NSA). Finally, different methods are used to carry out comparative experiments in different face databases. The results show that the method proposed in this paper has a higher correct recognition rate than the existing methods and has an obvious effect on the XM2VTS face database. This method not only improves the shortcomings of existing methods in dealing with complex face images but also provides a certain reference for face image feature extraction and recognition in complex environment.
Highlights
As a common biometric recognition method, face recognition (FR) has been well applied in the fields of mobile phone unlocking, secure payment, intelligent door lock, and so on
The kernel norm is a typical convex approximation of the rank minimization problem, the kernel norm minimization makes the solution quite different from the actual value [30]. In view of these situations, this paper presents a face image feature extraction and recognition method based on data dimensionality reduction algorithm. e nonparametric subspace analysis method is used to block the recognition image matrix and preextract the features
After obtaining the lowdimensional new pattern instead of the original image, the face image is digitally processed and recognized. This method is verified on the AR face database, ORL face database, and XM2VTS face database, and its recognition performance is better than other typical face recognition methods
Summary
As a common biometric recognition method, face recognition (FR) has been well applied in the fields of mobile phone unlocking, secure payment, intelligent door lock, and so on. Face recognition uses facial features to identify individuals. In these application fields, the information in the face image is lost due to the occlusion problem of the collected face image. E traditional algorithm based on feature extraction uses local or nonlocal facial features to train the model and uses the model to recognize the invisible face image [4, 5]. In face recognition, the deep learning method is to use the whole face image database to learn and predict the invisible face image without generating specific face features [6]. Most of the current FR approaches assume that multiple images of the same face are available for training their algorithms [7, 8]. When only one sample image can be used for system training, it is difficult to recognize others’ faces under different lighting conditions
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