Deep Residual Feature Quantization for 3D Face Recognition

3D face recognition (FR) has been successfully applied using Convolutional neural networks (CNN) which have demonstrated stunning results in diverse computer vision and image classification tasks. Learning CNNs, however, need to estimate millions of parameters that expect high-performance computing capacity and storage. To deal with this issue, we propose an efficient method based on the quantization of residual features extracted from ResNet-50 pre-trained model. The method starts by describing each 3D face using a convolutional feature extraction block, and then apply the Bag-of-Features (BoF) paradigm to learn deep neural networks (we call it Deep BoF). To do so, we apply Radial Basis Function (RBF) neurons to quantize the deep features extracted from the last convolutional layers. An SVM classifier is then applied to classify faces according to their quantized term vectors. The obtained model is lightweight compared to classical CNN and it allows classifying arbitrary-sized images. The experimental results on the FRGCv2 and Bosphorus datasets show the powerful of our method compared to state of the art methods.

Research on an Improved MB-LBP 3D Face Recognition Method

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.

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%.

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a significant improvement in the recognition rate, but small sample sizes represent a new problem. In this paper, we present an expression-invariant 3D face recognition method based on transfer learning and Siamese networks that can resolve the small sample size issue. First, a landmark detection method utilizing the shape index was employed for facial alignment. Then, a convolutional network (CNN) was constructed with transfer learning and trained with the aligned 3D facial data for face recognition, enabling the CNN to recognize faces regardless of facial expressions. Following that, the weighted trained CNN was shared by a Siamese network to build a 3D facial recognition model that can identify faces even with a small sample size. Our experimental results showed that the proposed method reached a recognition rate of 0.977 on the FRGC database, and the network can be used for facial recognition with a single sample.