Appearance Global and Local Structure Fusion for Face Image Recognition

Muntasa, Arif; Sirajudin, Indah Agustien; Purnomo, Mauridhi Hery

doi:10.12928/telkomnika.v9i1.678

Cited by 31 publications

(22 citation statements)

References 12 publications

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“…It is used to reduce the image dimensionality before the classification, or the recognition stage is conducted [8]. However, it has the weaknesses, i.e., the relationship of a variable to each other is always assumed a linear and global structure [9][10][11][12]. The appearance other methods such as two-dimensional Fisherface [1], Laplacianfaces [10], Gaussian Orthogonal Laplacianfaces [12,13] only produce the global structure and cannot represent the image features in detail, since they work on the input spaces to find the dominant features.…”

Section: Introductionmentioning

confidence: 99%

Homogeneous and Non-homogeneous Polynomial Based Eigenspaces to Extract the Features on Facial Images

2016

J Inf Process Syst

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High dimensional space is the biggest problem when classification process is carried out, because it takes longer time for computation, so that the costs involved are also expensive. In this research, the facial space generated from homogeneous and non-homogeneous polynomial was proposed to extract the facial image features. The homogeneous and non-homogeneous polynomial-based eigenspaces are the second opinion of the feature extraction of an appearance method to solve non-linear features. The kernel trick has been used to complete the matrix computation on the homogeneous and non-homogeneous polynomial. The weight and projection of the new feature space of the proposed method have been evaluated by using the three face image databases, i.e., the YALE, the ORL, and the UoB. The experimental results have produced the highest recognition rate 94.44%, 97.5%, and 94% for the YALE, ORL, and UoB, respectively. The results explain that the proposed method has produced the higher recognition than the other methods, such as the Eigenface, Fisherface, Laplacianfaces, and O-Laplacianfaces.

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Section: Introductionmentioning

confidence: 99%

Homogeneous and Non-homogeneous Polynomial Based Eigenspaces to Extract the Features on Facial Images

2016

J Inf Process Syst

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“…A biometric identification system is based on physical characteristics such as face [1], fingerprint, palmprint [2], eyes (iris, retina) and DNA. However, many of those characteristics are only suitable for ante mortem (AM) identification when a person to be identified is still alive.…”

Section: Introductionmentioning

confidence: 99%

Classification and Numbering of Dental Radiographs for an Automated Human Identification System

Yuniarti¹

2012

TELKOMNIKA

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“…Ear is large as compared to iris [1] and fingerprint and unlike them, the image acquisition of human ear is very easy as it can be captured from a distance without the cooperation of individual [2]. Human ear contains rich and stable features and it is more reliable than face as the structure of ear is not subject to change with the age and facial expressions [3]. The anatomy of human ear is given in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Personal Identification Using Ear Recognition

Tariq

Akram

2012

TELKOMNIKA

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Biometric authentication for personal identification is very popular now a days. Human ear recognition system is a new technology in this field. The change of appearance with the expression was a major problem in face biometrics Keywords: authentication, biometrics, ear recognition, Haar wavelet, personal identification IntroductionBiometrics deals with identification of individuals on the basis of their physiological and behavioral characteristics. Biometric technology is gaining popularity in this modern era for the purpose of security and other applications.The new feature in biometrics is human ear which is becoming popular. It has several advantages over other biometric technologies such as iris, fingerprints, face and retinal scans. Ear is large as compared to iris [1] and fingerprint and unlike them, the image acquisition of human ear is very easy as it can be captured from a distance without the cooperation of individual [2]. Human ear contains rich and stable features and it is more reliable than face as the structure of ear is not subject to change with the age and facial expressions [3]. The anatomy of human ear is given in Figure 1. It shows the standard features of human ear. It has been found that no two ears are exactly the same even that of identical twins [4], [5]. Therefore it appears that ear biometrics is a good solution for computerized human identification and verification systems. The major application of this technology is crime investigation. Ear features have been used for many years in the forensic sciences for recognition.There are many approaches in the literature for an automated ear recognition system. Alfred Iannarelli [6] was a pioneer in this field. He classified the ear into eight parts on the basis of 12 measurements. But this method was not suitable because of the difficulty of localization of anatomical points [6].

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Appearance Global and Local Structure Fusion for Face Image Recognition

Abstract: AbstrakAnalisis komponen utama (PCA)

Cited by 31 publications

References 12 publications

Homogeneous and Non-homogeneous Polynomial Based Eigenspaces to Extract the Features on Facial Images

Homogeneous and Non-homogeneous Polynomial Based Eigenspaces to Extract the Features on Facial Images

Classification and Numbering of Dental Radiographs for an Automated Human Identification System

Personal Identification Using Ear Recognition

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