Class-specific kernel linear regression classification for face recognition under low-resolution and illumination variation conditions

Chou, Yang Ting; Huang, Shih Ming; Yang, Jar Ferr

doi:10.1186/s13634-016-0328-0

Cited by 7 publications

(2 citation statements)

References 48 publications

(53 reference statements)

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“…Yang-Ting Chou et al Al. [17] reported an improvement in LRC restriction with the use of linear kernel regression classification algorithm (KLRC). It is aimed at creating a high dimensional feature space with the application of nonlinear mapping function for effective linear regression.…”

Section: Fig 1: Block Diagram Of Lr-frmentioning

confidence: 99%

Low Resolution Face Recognition System Performance for Basic Feature Space Methods with Various Illumination Normalization Techniques

Kapse*,

Kumar

2019

IJITEE

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The Face recognition research identified with the field of automated surveillance systems in real life application has pulled in more consideration today with extensive use of vision cameras in biometrics and surveillance applications, globally. The small size images or poor quality images are generally Low Resolution images. Authenticating faces, with variation in pose, illumination, disguise and more, from such Low-resolution images, is the main purpose of Low Resolution Face Recognition (LR FR) system. We have assessed the LR FR system for various basic feature space techniques like PCA, LDA and Fisherface. Different illumination normalization techniques are applied on the cropped Yale face database prior to feature extraction and identification. In our work the low-resolution images, of size 32x32, are the down-sampled versions high-resolution facial images from cropped Yale face database. Our experiment demonstrates the encouraging performance, with recognition accuracy as 97.43%, on Low Resolution, Low quality face images.

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Section: Fig 1: Block Diagram Of Lr-frmentioning

confidence: 99%

Low Resolution Face Recognition System Performance for Basic Feature Space Methods with Various Illumination Normalization Techniques

Kapse*,

Kumar

2019

IJITEE

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“…The other face recognition algorithms used in the experiments include principal component analysis (PCA) [10,11], linear discriminant analysis (LDA) [15], linear regression classification (LRC) [22], modular linear regression-based classification (MLRC) [22], sparse representation classification (SRC) [36,37], locality preserving projection (LPP) [38], neighboring preserving embedding (NPE) [39], improved principal component regression (IPCR) [40], unitary regression classification (URC) [41], linear discriminant regression classification (LDRC) [42], and kernel linear regression classification (KLRC) [43] methods. From Fig.…”

Section: Experiments For Face Recognitionmentioning

confidence: 99%

Multi-feature shape regression for face alignment

Yang

Chen

Chung

et al. 2018

EURASIP J. Adv. Signal Process.

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For smart living applications, personal identification as well as behavior and emotion detection becomes more and more important in our daily life. For identity classification and facial expression detection, facial features extracted from face images are the most popular and low-cost information. The face shape in terms of landmarks estimated by a face alignment method can be used for many applications including virtual face animation and real face classification. In this paper, we propose a robust face alignment method based on the multi-feature shape regression (MSR), which is evolved from the explicit shape regression (ESR) proposed in Cao et al. (Int, Vis, 2014, 107:177-190, Comput). The proposed MSR face alignment method successfully utilizes color, gradient, and regional information to increase accuracy of landmark estimation. For face recognition algorithms, we further suggest a face warping algorithm, which can cooperate with any face alignment algorithm to adjust facial pose variations to improve their recognition performances. For performance evaluations, the proposed and the existing face alignment methods are compared on the face alignment database. Based on alignment-based face recognition concept, the face alignment methods with the proposed face warping method are tested on the face database. Simulation results verify that the proposed MSR face alignment method achieves better performances than the other existing face alignment methods.

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A Kernel-Based Probabilistic Collaborative Representation for Face Recognition

et al. 2020

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A novel classifier for face recognition using an improved probabilistic collaborative representation named IPCR is proposed in this paper. The purpose of this paper is to improve the accuracy of face recognition. The testing sample is assumed to be linearly combined by a part of training samples in feature space. There is two-phase framework in IPCR. In the first phase, an adjusted parameter of the nearest neighbors of the samples is chosen for classification. In the second phase, a linear combination of the features and the sparse coefficients are used for new patterns. In the process of two-phase framework, the weight matrix is obtained according to the distance between all the training samples and each testing sample, and then it is applied to weight probabilistic collaborative representation coefficients. The kernel trick is implemented for the high-dimensional nonlinear information instead of linear information of data to improve the class separability. The second classifier named KPCR uses a kernel probabilistic collaborative representation for face recognition. Several renowned face databases, e.g., AR, GT, PIE, FERET, and LFW-crop are used for evaluating the performances of the proposed classifiers. The experimental results demonstrate that the proposed classifiers outperform the collaborative representation-based classification (CRC), the probabilistic collaborative representation-based classifier (ProCRC), and the other stateof-the-art classifiers in recognition accuracy.

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Class-specific kernel linear regression classification for face recognition under low-resolution and illumination variation conditions

Cited by 7 publications

References 48 publications

Low Resolution Face Recognition System Performance for Basic Feature Space Methods with Various Illumination Normalization Techniques

Low Resolution Face Recognition System Performance for Basic Feature Space Methods with Various Illumination Normalization Techniques

Multi-feature shape regression for face alignment

A Kernel-Based Probabilistic Collaborative Representation for Face Recognition

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