Human Face Recognition Algorithm Research Articles

Optimization of the Data Representation Integrated Form in the Viola-Jones Algorithm for a Person’s Face Search

It is shown that the task of a person’s face recognition is one of the most popular at the present time. Its effective solution determines the reliability of many modern systems of personal identification, security, access control, and video surveillance at thermal and nuclear power plant stations. The speed of facial recognition algorithms is one of the key factors that determine the possibility of using for solving specific practical problems. It is shown that one of the methods for increasing the speed of facial recognition algorithms is the use of an integral form of data representation. The Viola-Jones algorithm is identified as one of the classic approaches to solving the problem of face recognition. The main limitations for using the classical integral form of data representation are shown. Rotation of the head is identified as the main factor leading to a significant increase in the error in determining the brightness indicators for the most informative areas of the face. An approach based on the use of modified integrated forms for image frame data representation is proposed. These forms are oriented to the fast processing of information in the presence of a significant rotation of the head. The most important range of possible changes in the angle of rotation of the head is identified. The data structure is considered in case of using the modified integral form. Experimentally confirmed the possibility of using a limited number of modified forms of data representation while maintaining the high reliability of the work of the human face recognition algorithm.

Research into Human Face Recognition Algorithm Based on Active Appearance Model Feature Positioning

Active Appearance Model (AAM) is a valid statistical algorithm for human face alignment, which composes of two parts, namely AAM sub-space model and AAM searching process. AAM has three sub-spaces, namely form sub-space, texture sub-space and surface sub-face. As it is stated above, AAM is built on the dot distribution model. Different from ASM, it not only conducts statistical analysis of textures through the shape information, but also explores the connection between the shape and the texture. During the training period, it mainly targets at finding out the connection between model parameter changes and changes of shapes and textures. In terms of new image searching, model parameters can be continuously adjusted according to the connection so as to make the composite image approximate the new one as much as possible. The shape and the texture at the moment are regarded at the shape and the texture of the new image.

Deep Perusal of Human Face Recognition Algorithms from Facial Snapshots

Face recognition having so many application in security, access control, gaming, aging effect, health issues, and internet communication and so on. It has been perform by using different face recognition algorithms on different field of application. When we use face recognition in real-world scenarios with unfavorable conditions such as occlusion and pose variations, illumination and expressions. Here also study the challenges during face recognition under immoral conditions such as facial expression recognition with poses variations, occlusion and lightning condition. Basically it proposes several possible future direction which is excluded from the challenges used in it. Thus, it is a beneficial developing ping point for research project on face recognitions able to be used for a practical purpose or in several ways.

Compressed sensing with MCT and I(2D)2PCA processing for efficient face recognition

This article describes an effective human face recognition algorithm. Even though the principle component analysis (PCA) is one of the most common feature extraction methods, it is not suitable to implement a real-time embedded system for face recognition because large amount of computational load and memory capacity are necessary. To overcome this problem, we employ the incremental two-directional two-dimensional PCA (I(2D)2PCA) which is a combination of the (2D)2PCA to demand much less computational complexity than the conventional PCA and the incremental PCA (IPCA) to adapt the eigenspace only by using a new incoming sample datum without reusing of all the previous trained data. Furthermore, the modified census transform (MCT), a local normalization method useful for real-world application and implementation in an embedded system, is adopted to address robustness to illumination variations. To achieve better recognition accuracy with less computational load, the processed features are classified by the compressive sensing approach using l2–minimization. Experimental results on the Yale Face Database B show that the described system using the l2–minimization-based classification method for input data processed by the I(2D)2PCA and the MCT provided efficient and robust face recognition. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 133–139, 2013

B2DPCA和ELM人脸识别算法研究

A new human face recognition algorithm was proposed based on B2DPCA(bidirectional two dimensional principal component analysis) and ELM(extreme learning machine).This method was based on curvelet image decomposition of human faces and an improved dimensionality reduction technique.Discriminative feature sets were generated by using B2DPCA to train and test ELM classifier.The recognition accuracy can be improved by using this method.Extensive experiments were performed by using databases and results were compared with state of the existing techniques.The results showed recognition accuracy and minimal dependence on the number of prototypes were significantly improved by using B2DPCA and ELM algorithm.The local characteristics and global information based on curvelet decomposition are expected to apply to the recognition accuracy and speed of classification in the future.

Human face recognition based on multidimensional PCA and extreme learning machine

In this work, a new human face recognition algorithm based on bidirectional two dimensional principal component analysis (B2DPCA) and extreme learning machine (ELM) is introduced. The proposed method is based on curvelet image decomposition of human faces and a subband that exhibits a maximum standard deviation is dimensionally reduced using an improved dimensionality reduction technique. Discriminative feature sets are generated using B2DPCA to ascertain classification accuracy. Other notable contributions of the proposed work include significant improvements in classification rate, up to hundred folds reduction in training time and minimal dependence on the number of prototypes. Extensive experiments are performed using challenging databases and results are compared against state of the art techniques.

A new algorithm for generalized optimal discriminant vectors

A study has been conducted on the algorithm of solving generalized optimal set of discriminant vectors in this paper. This paper proposes an analytical algorithm of solving generalized optimal set of discriminant vectors theoretically for the first time. A lot of computation time can be saved because all the generalized optimal sets of discriminant vectors can be obtained simultaneously with the proposed algorithm, while it needs no iterative operations. The proposed algorithm can yield a much higher recognition rate. Furthermore, the proposed algorithm overcomes the shortcomings of conventional human face recognition algorithms which were effective for small sample size problems only. These statements are supported by the numerical simulation experiments on facial database of ORL.