High Face Recognition Accuracy Research Articles

Single-Sample Face Recognition Based on LPP Feature Transfer

Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many machine learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal with conflicting concerns of the small sample size and high dimensionality in one-sample face recognition is critical for its achievable recognition accuracy and feasibility in practice. Being different from the conventional methods for global face recognition based on generalization ability promotion and local face recognition depending on image segmentation, a single-sample face recognition algorithm based on locality preserving projection (LPP) feature transfer is proposed here. First, transfer sources are screened to obtain the selective sample source using the whitened cosine similarity metric. Second, we project the vectors of source faces and target faces into feature subspace by LPP, respectively, and calculate the feature transfer matrix to approximate the mapping relationship on source faces and target faces in subspace. Then, the feature transfer matrix is used on training samples to transfer the original macro characteristics to target macro characteristics. Finally, the nearest neighbor classifier is used for face recognition. Our results based on popular databases FERET, ORL, and Yale demonstrate the superiority of the proposed LPP feature transfer-based one-sample face recognition algorithm when compared with popular single-sample face recognition algorithms, such as (PC) $^{2}\text{A}$ and Block FLDA.

Data uncertainty in face recognition.

The image of a face varies with the illumination, pose, and facial expression, thus we say that a single face image is of high uncertainty for representing the face. In this sense, a face image is just an observation and it should not be considered as the absolutely accurate representation of the face. As more face images from the same person provide more observations of the face, more face images may be useful for reducing the uncertainty of the representation of the face and improving the accuracy of face recognition. However, in a real world face recognition system, a subject usually has only a limited number of available face images and thus there is high uncertainty. In this paper, we attempt to improve the face recognition accuracy by reducing the uncertainty. First, we reduce the uncertainty of the face representation by synthesizing the virtual training samples. Then, we select useful training samples that are similar to the test sample from the set of all the original and synthesized virtual training samples. Moreover, we state a theorem that determines the upper bound of the number of useful training samples. Finally, we devise a representation approach based on the selected useful training samples to perform face recognition. Experimental results on five widely used face databases demonstrate that our proposed approach can not only obtain a high face recognition accuracy, but also has a lower computational complexity than the other state-of-the-art approaches.

RGB-D Face Recognition With Texture and Attribute Features

Face recognition algorithms generally utilize 2D images for feature extraction and matching. To achieve higher resilience toward covariates, such as expression, illumination, and pose, 3D face recognition algorithms are developed. While it is challenging to use specialized 3D sensors due to high cost, RGB-D images can be captured by low-cost sensors such as Kinect. This research introduces a novel face recognition algorithm using RGB-D images. The proposed algorithm computes a descriptor based on the entropy of RGB-D faces along with the saliency feature obtained from a 2D face. Geometric facial attributes are also extracted from the depth image and face recognition is performed by fusing both the descriptor and attribute match scores. The experimental results indicate that the proposed algorithm achieves high face recognition accuracy on RGB-D images obtained using Kinect compared with existing 2D and 3D approaches.

IMPROVED REAL-TIME FACE RECOGNITION BASED ON THREE LEVEL WAVELET DECOMPOSITION-PRINCIPAL COMPONENT ANALYSIS AND MAHALANOBIS DISTANCE

The development of research in the field of real-time face recognition is a study that is being developed in the last decade. Face recognition is used to identify person from an image or video. Recognition rate and computation time of real-time face recognition is one of the big challenges that must be developed. This study proposes a model of face recognition using the method of feature extraction by combining three level wavelet decomposition and Principal Component Analysis (PCA) and using the method of mahalanobis distance for the classification section (3WPCA-MD). A 3-level wavelet decomposition is used to decompose images by reducing the resolution used for those images. Using wavelet decomposition up to level 3 will produce an image with a very low resolution so as to reduce the value of the resulting computation time to be processed using PCA. Mahalanobis distance method is used to determine the degree of similarity among the features to produce a more optimal face recognition. Based on the results of experiments that have been done, they generated improved face recognition with high face recognition accuracy of up to 96% in average and produced faster computation results of face recognition if compared to ordinary PCA method. The average computation speed value obtained using the method of 3WPCA-MD was 5-7 milli-second (ms) for each face recognition process.

Face Recognition Based on Maximum Spanning Tree Kernel and Gray Kernel

Face Recognition has been recognized as a major research field in pattern recognition and computer vision. This technique is widely adopted, because of its unique convenience, economy and high accuracy compared to other biological recognition techniques. An interesting and important challenge is thus to investigate high-efficient recognition algorithm. The introduction of kernel methods in pattern recognition has been received significant attentions in the recent several years, and gray kernel and graph kernel are two popular approaches. The paper proposes maximum spanning tree kernel and region histogram intersection kernel; moreover, experiments demonstrate that higher face recognition accuracy can be achieved by multiple kernels which are the combination of them.

Combination of direct matching and collaborative representation for face recognition

It has been proved that representation-based classification (RBC) can achieve high accuracy in face recognition. However, conventional RBC has a very high computational cost. Collaborative representation proposed in [1] not only has the advantages of RBC but also is computationally very efficient. In this paper, a combination of direct matching of images and collaborative representation is proposed for face recognition. Experimental results show that the proposed method can always classify more accurately than collaborative representation! The underlying reason is that direct matching of images and collaborative representation use different ways to calculate the dissimilarity between the test sample and training sample. As a result, the score obtained using direct matching of images is very complementary to the score obtained using collaborative representation. Actually, the analysis shows that the matching scores generated from direct matching of images and collaborative representation always have a low correlation. This allows the proposed method to exploit more information for face recognition and to produce a better result.

Continuous Pose Normalization for Pose-Robust Face Recognition

Pose variation is a great challenge for robust face recognition. In this paper, we present a fully automatic pose normalization algorithm that can handle continuous pose variations and achieve high face recognition accuracy. First, an automatic method is proposed to find pose-dependent correspondences between 2-D facial feature points and 3-D face model. This method is based on a multi-view random forest embedded active shape model. Then we densely map each pixel in the face image onto the 3-D face model and rotate it to the frontal view. The filling of occluded face regions is guided by facial symmetry. Recognition experiments were conducted on the two western databases CMU-PIE, FERET and one eastern database CAS-PEAL. Currently the algorithm has been trained with pose variation up to ±50° in yaw. Our algorithm not only achieves high recognition accuracy for learnt poses but also shows good generalizability for extreme poses. Furthermore, it suggests the promising application to people of different races.

Face recognition method based on support vector machine and particle swarm optimization

Face recognition belongs to the problem of non-linear, which increases the difficulty of its recognition. Support vector machine (SVM) is a novel machine learning method, which can find global optimum solutions for problems with small training samples and non-linear, so support vector machine has a good application prospect in face recognition. In the study, the novel face recognition method based on support vector machine and particle swarm optimization (PSO-SVM) is presented. In PSO-SVM, PSO is used to simultaneously optimize the parameters of SVM. FERET human face database is adopted to study the face recognition performance of PSO-SVM, and the proposed method is compared with SVM, BPNN. The experimental indicates that PSO-SVM has higher face recognition accuracy than normal SVM, BPNN. Therefore, PSO-SVM is well chosen in face recognition.