Improved Face Recognition Algorithm Research Articles

An improved face recognition algorithm and its application in attendance management system

Abstract Face Recognition is a computer application that is capable of detecting, tracking, identifying or verifying human faces from an image or video captured using a digital camera. Although lot of progress has been made in domain of face detection and recognition for security, identification and attendance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. These issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose etc. This research paper presents a new method using Local Binary Pattern (LBP) algorithm combined with advanced image processing techniques such as Contrast Adjustment, Bilateral Filter, Histogram Equalization and Image Blending to address some of the issues hampering face recognition accuracy so as to improve the LBP codes, thus improve the accuracy of the overall face recognition system. Our experiment results show that our method is very accurate, reliable and robust for face recognition system that can be practically implemented in real-life environment as an automatic attendance management system.

A modified technique for face recognition under degraded conditions

In this paper an improved face recognition algorithm under degrading conditions is proposed. The proposed algorithm uses a combination of preprocessing techniques coupled with discriminative feature extractors to obtain the best distinctive features for classification. Preprocessing approach is the fusion of multi-scale Weber and enhanced complex wavelet transform. Combination of multiple feature extraction based on Gabor filters, block-based local phase quantization (LPQ) coupled with principal component analysis (PCA) proved to be very effective to improve correct rate of recognition. We have also used two known classifiers, extreme learning machine (ELM), and sparse classifier (SC), and fused their outputs to obtain best recognition rate. Experimental results show improved performance of proposed algorithm under poor illumination, partial occlusion and low-quality images in uncontrolled conditions. Our best recognition results using second version of face recognition grand challenge (FRGC 2.0.4) which is the most challenging database, indicated more than 28% improvement over previous works.

A Face Recognition Algorithm Based on Improved Contourlet Transform and Principle Component Analysis

As the internet keeping developing, face recognition has become a research hotspot in the field of biometrics. This paper proposes an improved face recognition algorithm that reduces the influence of illumination and posture variations. First, face images are transformed by using the improved contourlet transform method to get low frequency sub-band images and high frequency sub-band images. Then this paper uses the principal component analysis to extract main features. Finally, combines these statistic features together as feature vector and recognize face images. Analysis, experiment and proof on the ORL face database and the Yale face database show that this algorithm is better able to recognize faces, reduce the influence of illumination and posture variations and increase the efficiency of face recognition.

An Improved Face Recognition Algorithm Based on SIFT and LBP

A new face image feature extraction and recognition algorithm based on Scale Invariant Feature Transform (SIFT) and Local Linary Patterns (LBP) is proposed in this paper. Firstly, a set of keypoints are extracted from images by using the SIFT algorithm; Secondly, each keypoint is described by LBP patterns; Finally, a combination of the global and local similarity is adopted to calculate the matching results for face images. Calculation results show that the algorithm can reduce the matching dimension of feature points, improve the recognition rate and perspective; it has nice robustness against the interferences such as rotation, lighting and expression.

An Improved Face Recognition Algorithm Using Adjacent Pixel Intensity Difference Quantization

An Improved Face Recognition Algorithm Using Adjacent Pixel Intensity Difference Quantization