Real-Time Face Recognition Using Eigenfaces

In recent years considerable progress has been made in the area of face recognition. Through the development of techniques like eigenfaces, computer can now compute favourably with humans in many face recognition tasks, particularly those in which large databases of faces must be searched. Whilst these methods perform extremely well under constrained conditions, the problem of face recognition under gross variations in expressions, view and lighting remains largely unsolved. This paper details the design of a real-time face recognition system aimed at operating in less constrained environments. The system is capable of single scale recognition with an accuracy of 94% at 2 frames per second. A description of the system's performance and the issues and problems faced during its development is given

An Exploration of the Feasibility of FPGA Implementation of Face Recognition Using Eigenfaces

Biometric identification has been a major force since 1990\u27s. There are different types of approaches for it; one of the most significant approaches is face recognition. Over the past two decades, face recognition techniques have improved significantly, the main focus being the development of efficient algorithm. The state of art algorithms with good recognition rate are implemented using programming languages such as C++, JAVA and MATLAB, these requires a fast and computationally efficient hardware such as workstations. If the face recognition algorithms could be written in a Hardware Description Language, they could be implemented in an FPGA. In this thesis we have choose the eigenfaces algorithm, since it is simple and very efficient, this algorithm is first solved analytically, and then the architecture is designed for FPGA implementation. We then develop the Verilog module for each of these modules and test their functionality using a Verilog Simulator and finally we discuss the feasibility of FPGA implementation. Implementing the face recognition technology in an FPGA would mean that they would require relatively low power and the size is drastically reduced when compared to the workstations. They would also be much faster and efficient, since they are specifically designed for face recognition

An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image

Biometrics based personal identification is regarded as an effective method for automatically recognizing, with a high confidence a person’s identity. A multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically better recognition performance compare to system based on a single biometric modality. This paper proposes an authentication method for a multimodal biometric system identification using two traits i.e. face and palmprint. The proposed system is designed for application where the training data contains a face and palmprint. Integrating the palmprint and face features increases robustness of the person authentication. The final decision is made by fusion at matching score level architecture in which features vectors are created independently for query measures and are then compared to the enrolment template, which are stored during database preparation. Multimodal biometric system is developed through fusion of face and palmprint recognition

Optimizing Face Recognition Using PCA

Principle Component Analysis PCA is a classical feature extraction and data representation technique widely used in pattern recognition. It is one of the most successful techniques in face recognition. But it has drawback of high computational especially for big size database. This paper conducts a study to optimize the time complexity of PCA (eigenfaces) that does not affects the recognition performance. The authors minimize the participated eigenvectors which consequently decreases the computational time. A comparison is done to compare the differences between the recognition time in the original algorithm and in the enhanced algorithm. The performance of the original and the enhanced proposed algorithm is tested on face94 face database. Experimental results show that the recognition time is reduced by 35% by applying our proposed enhanced algorithm. DET Curves are used to illustrate the experimental results.Comment: 9 page

Automatic face recognition of video sequences using self-eigenfaces

The objective of this work is to provide an efficient face recognition scheme useful for video indexing applications. In particular we are addressing the following problem: given a set of known images and given a video sequence to be indexed, find where the corresponding persons appear in the sequence. Conventional face detection schemes are not well suited for this application and alternate and more efficient schemes have to be developed. In this paper we have modified our original generic eigenface-based recognition scheme presented in [1] by introducing the concept of selfeigenfaces. The resulting scheme is very efficient to find specific face images and to cope with the different face conditions present in a video sequence. The main and final objective is to develop a tool to be used in the MPEG-7 standardization effort to help video indexing activities. Good results have been obtained using the video test sequences used in the MPEG-7 evaluation group.Peer ReviewedPostprint (published version