Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

Influence of facial expression and viewpoint variations on face recognition accuracy by different face recognition algorithms

Abstract: Face recognition is a personal identification method using biometrics that is gaining the attention in this research field. The face recognition process can be done without the human and devices interaction, so it can be applied in several applications. In additions, the face recognition systems are typically implemented at different places in unconstrained environments. Hence, the study of the factors that impact the face recognition accuracy is an interesting and challenging topic. In the class attendance checking system using face recognition, there are variations of three factors that possibly affect the accuracy of the system; facial expressions, and face viewpoints. This study intends to compare facial recognition accuracy of three well-known algorithms namely Eigenfaces, Fisherfaces, and LBPH. The experiments conducted in the respects of the variation of facial expressions, and face viewpoints in the actual classroom. The results of the experiment demonstrated that LBPH is the most precise algorithm which achieves 81.67% of accuracy in still-image-based testing. The facial expression that has the most impact on accuracy is the grin, and face viewpoints that affect accuracy are looking down and tilting left, and right respectively. Therefore, LBPH is the most suitable algorithm to apply in a class attendance checking system after considering the accuracy.

An Enhanced Histogram Matching Approach Using the Retinal Filter's Compression Function for Illumination Normalization in Face Recognition

Abstract: Although many face recognition techniques have been proposed, recent evaluations in FRVT2006 conclude that relaxing the illumination condition has a dramatic effect on their recognition performance. Among many illumination normalization approaches, histogram matching (HM) is considered one of the most common image-processing-based approaches to cope with illumination. This paper introduces a new illumination normalization approach based on enhancing the image resulting from the HM using the gamma correction and the Retinal filter's compression function; we call it GAMMA-HM-COMP approach. Rather than many other approaches, the proposed one proves its flexibility to different face recognition methods and the suitability for real-life systems in which perfect aligning of the face is not a simple task. The efficiency of the proposed approach is empirically demonstrated using both a PCA-based (Eigenface) and a frequency-based (Spectroface) face recognition methods on both aligned and non-aligned versions of Yale B database. It leads to average increasing in recognition rates ranges from 4 ~ 7 % over HM alone.

Curvelet Based Feature Extraction

Abstract: Designing a completely automatic and efficient face recognition system is a grand challenge for biometrics, computer vision and pattern recognition researchers Generally, such a recognition system is able to perform three subtasks: face detection, feature extraction and classification We’ll put our focus on feature extraction, the crucial step prior to classification The key issue here is to construct a representative feature set that can enhance system-performance both in terms of accuracy and speed At the core of machine recognition of human faces is the extraction of proper features Direct use of pixel values as features is not possible due to huge dimensionality of the faces Traditionally, Principal Component Analysis (PCA) is employed to obtain a lower dimensional representation of the data in the standard eigenface based methods [Turk and Pentland 1991] Though this approach is useful, it suffers from high computational load and fails to well-reflect the correlation of facial features The modern trend is to perform multiresolution analysis of images This way, several problems like, deformation of images due to in-plane rotation, illumination variation and expression changes can be handled with less difficulty Multiresolution ideas have been widely used in the field of face recognition The most popular multiresolution analysis tool is the Wavelet Transform In wavelet analysis an image is usually decomposed at different scales and orientations using a wavelet basis vector Thereafter, the component corresponding to maximum variance is subjected to ‘further operation’ Often this ‘further operation’ includes some dimension reduction before feeding the coefficients to classifiers like Support Vector Machine (SVM), Neural Network (NN) and Nearest Neighbor This way, a compact representation of the facial images can be achieved and the effect of variable facial appearances on the classification systems can also be reduced The wide-spread popularity of wavelets has stirred researchers’ interest in multiresolution and harmonic analysis Following the success of wavelets, a series of multiresolution, multidimensional tools, namely contourlet, curvelet, ridgelet have been developed in the past few years In this chapter, we’ll concentrate on Digital Curvelet Transform First, the theory of curvelet transform will be discussed in brief Then we'll talk about the potential of curvelets as a feature descriptor, looking particularly into the problem of image-based face recognition Some experimental results from recent scientific works will be provided for ready reference 3

The New Face Recognition Technique With the use of PCA and LDA.

Abstract: Image recognition using various image classifiers is an active research area. In this paper we will describe a new face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminate Analysis). The new method consists of two steps: first we project the face image from the original vector space into a face subspace by using PCA technique and finally we use LDA to obtain a linear classifier. The idea of combination of two methods proved enough good improvement in the generalization capability of LDA where only few samples per class were available. BY using ORL dataset we observed a significant improvement compared with original images were fed directly to the LDA classifier. The newly proposed hybrid classifier has provided a useful framework means for general image recognition tasks as well.

Web and mobile based facial recognition security system using Eigenfaces algorithm

Abstract: The study was done in accordance with the deviation from the tradition to modernization of human identification with the purpose of both human convenience and technology maximization. The study aimed to proposed a facial recognition security system, as the main access level for private establishments in order to prevent unauthorized entry of an individual. Facial Recognition delivers convenience, for it is a concrete identification that an individual can have without the need of badges and many other identification cards. It administers efficiency on both the organization and its people. Problems related to stolen, borrowed and lost IDs, are eliminated; thus reducing the cost for password administration or renewal.