Fast Face Recognition Algorithm Research Articles

3D Face Recognition Based on an Attention Mechanism and Sparse Loss Function

Face recognition is one of the essential applications in computer vision, while current face recognition technology is mainly based on 2D images without depth information, which are easily affected by illumination and facial expressions. This paper presents a fast face recognition algorithm combining 3D point cloud face data with deep learning, focusing on key part of face for recognition with an attention mechanism, and reducing the coding space by the sparse loss function. First, an attention mechanism-based convolutional neural network was constructed to extract facial features to avoid expressions and illumination interference. Second, a Siamese network was trained with a sparse loss function to minimize the face coding space and enhance the separability of the face features. With the FRGC face dataset, the experimental results show that the proposed method could achieve the recognition accuracy of 95.33%.

Multimedia based fast face recognition algorithm of speed up robust features

For the problem that the HAAR descriptors in the speed up robust feature (SURF) algorithm cannot make full use of the information around the feature points, the K-Mean clustering technology is used in this paper to improve the SURF, thus proposing a new face recognition algorithm. Firstly, the problem that the main direction is too dependent on the direction of the local area is avoided by expanding the scope of the main direction; then the information around the subblock that is masked by the 3 × 3 window template is made full use of to construct a descriptor with stronger recognition ability; finally, the problems of excessive time consumed and incorrect matching of interest points are solved by introducing the K-Mean clustering idea. The results of the experiment on FERET and Yale face database show that the proposed algorithm has higher recognition rate and efficiency than other face recognition techniques.

New fast face recognition algorithm based on Gabor filter

Concerning the disadvantage of traditional face recognition algorithm,such as high dimension of extracted feature,a great deal of computation,a fast face recognition algorithm was proposed.The algorithm integrated the half face recognition scheme,Gabor filter,Gabor features selecting method based on mutual information,and the nearest neighbor method for frontal face recognition.The face images in training set and testing set were divided into the left half and the right half,one half of the face images was chosen by entropy maximum.The features of the face images were extracted by Gabor filter.Then the rank of discriminating capabilities of features can be estimated by evaluating the classification error on intra-set and extra-set based on weak classifier built by single feature.The Gabor features with small errors were selected.And at the same time,the mutual information between the selected features was examined.The nearest neighbor method was used to recognize the frontal face.The experimental results show that the proposed method has higher accuracy than the traditional half face recognition algorithm,and is of lower computational complexity than the traditional Gabor filter algorithm.

A Codebook Design Method for Robust VQ-Based Face Recognition Algorithm

In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, firstly we theoretically create a systematically organized codebook. Combined with another codebook created by Kohonen’s Self-Organizing Maps (SOM) method, an optimized codebook consisted of 2×2 codevectors for facial images is generated. Experimental results show face recognition using such a codebook is more efficient than the codebook consisted of 4×4 codevector used in conventional algorithm. The highest average recognition rate of 98.6% is obtained for 40 persons’ 400 images of publicly available face database of AT&T Laboratories Cambridge containing variations in lighting, posing, and expressions. A table look-up (TLU) method is also proposed for the speed up of the recognition processing. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition.