New Principles in Algorithm Design for Problems of Face Recognition

A New Evolutionary-Incremental Framework for Feature Selection

Advances in Artificial Intelligence ◽

10.1155/2014/679847 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Mohamad-Hoseyn Sigari ◽

Muhammad-Reza Pourshahabi ◽

Hamid-Reza Pourreza

Keyword(s):

Feature Selection ◽

Face Recognition ◽

Algorithm Design ◽

Feature Selection Method ◽

Recognition Algorithm ◽

Comparison Result ◽

Open Problems ◽

Invasive Weed ◽

Np Hard Problem ◽

Robust Face Recognition

Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection. The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length. Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection.

Download Full-text

Research on an SVM Coupling Algorithm of Image Recognition

10.20944/preprints201610.0038.v1 ◽

2016 ◽

Author(s):

Ai-Min Yang ◽

Yang Han ◽

Jin-Ze Li ◽

Yu-Hang Pan ◽

Lei-Jie Shen ◽

...

Keyword(s):

Face Recognition ◽

Recognition Accuracy ◽

Algorithm Design ◽

Principal Component ◽

Facial Feature ◽

Face Image ◽

Support Vector ◽

Svm Classifier ◽

Face Image Processing ◽

Coupling Algorithm

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Download Full-text

Face Inversion Effect Emerges Under Critical Configural Discrepancy

Swiss Journal of Psychology ◽

10.1024/1421-0185/a000018 ◽

2010 ◽

Vol 69 (3) ◽

pp. 161-167 ◽

Cited By ~ 3

Author(s):

Jisien Yang ◽

Adrian Schwaninger

Keyword(s):

Face Recognition ◽

Face Processing ◽

Underlying Mechanism ◽

Inversion Effect ◽

Configural Processing ◽

Major Contributor ◽

Face Inversion Effect ◽

Configural Information ◽

Face Inversion ◽

The Face

Configural processing has been considered the major contributor to the face inversion effect (FIE) in face recognition. However, most researchers have only obtained the FIE with one specific ratio of configural alteration. It remains unclear whether the ratio of configural alteration itself can mediate the occurrence of the FIE. We aimed to clarify this issue by manipulating the configural information parametrically using six different ratios, ranging from 4% to 24%. Participants were asked to judge whether a pair of faces were entirely identical or different. The paired faces that were to be compared were presented either simultaneously (Experiment 1) or sequentially (Experiment 2). Both experiments revealed that the FIE was observed only when the ratio of configural alteration was in the intermediate range. These results indicate that even though the FIE has been frequently adopted as an index to examine the underlying mechanism of face processing, the emergence of the FIE is not robust with any configural alteration but dependent on the ratio of configural alteration.

Download Full-text

Perceptual Effects and Recollective Experience in Face Recognition

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169.52.3.224 ◽

2005 ◽

Vol 52 (3) ◽

pp. 224-231 ◽

Cited By ~ 7

Author(s):

Chrisanthi Nega

Keyword(s):

Face Recognition ◽

Recognition Memory ◽

Congruency Effect ◽

Facial Recognition ◽

Memory Systems ◽

Study Time ◽

Perceptual Fluency ◽

Recollective Experience ◽

Trace Strength ◽

Size Congruency

Abstract. Four experiments were conducted investigating the effect of size congruency on facial recognition memory, measured by remember, know and guess responses. Different study times were employed, that is extremely short (300 and 700 ms), short (1,000 ms), and long times (5,000 ms). With the short study time (1,000 ms) size congruency occurred in knowing. With the long study time the effect of size congruency occurred in remembering. These results support the distinctiveness/fluency account of remembering and knowing as well as the memory systems account, since the size congruency effect that occurred in knowing under conditions that facilitated perceptual fluency also occurred independently in remembering under conditions that facilitated elaborative encoding. They do not support the idea that remember and know responses reflect differences in trace strength.

Download Full-text