Race Classification From Face Images Using Local Descriptors

Muhammad, Ghulam; Hussain, Muhammad; Alenezy, Fatmah; Bebis, George; Mirza, Anwar M.; Aboalsamh, Hatim

doi:10.1142/s0218213012500194

Cited by 26 publications

(13 citation statements)

References 16 publications

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“…Experiments show that such an ICA-based system achieves a classification rate of 82.5%. Weber local descriptors, wavelet, and local binary patterns have been investigated respectively in [113], [114], [115], classification results on five race groups from the FERET database showed their effectiveness and superior performance over holistic PCA. Other lowlevel based features such as gradient direction histograms, multiple convolution network generated features (Fig.…”

Section: Racial Feature Representationmentioning

confidence: 99%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

158

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Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face.

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Section: Racial Feature Representationmentioning

confidence: 99%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

158

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“…LBP histogram (LBPH) features were extracted in [26] to estimate Asian and non-Asian ethnicity by means of AdaBoost classifiers. LBP was also used in [58] and then fused with Weber local descriptors through concatenation to produce a more powerful set of features to be supplied to a minimum distance classifier for the final race estimation. WLD histograms were employed in [59] demonstrating that they outperform PCA-based holistic methods.…”

Section: Racementioning

confidence: 99%

A study on different experimental configurations for age, race, and gender estimation problems

Carcagnì

Coco

Cazzato

et al. 2015

J Image Video Proc.

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This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In particular, a recently introduced common framework is the starting point of the study: it includes an initial facial detection, the subsequent facial traits description, the data reduction step, and the final classification step. The algorithmic configurations are featured by different descriptors and different strategies to build the training dataset and to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available datasets and image sequences specifically acquired in order to evaluate the performance even under real-world conditions, i.e., in the presence of scaling and rotation.

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“…RAG-MCFP-DCNNs -100 96.4 Manesh et al [38] FERET [31] and CAS-PEAL [32] 98 96 Muhammed et al [86] FERET [31] 99.4 -Chen and Ross [43] CAS-PEAL [32] 98.7 -Anwar and Naeem [42] FERET 98.…”

Section: Databasementioning

confidence: 99%

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Khan

Attique

Khan

et al. 2020

Sensors

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Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.

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Race Classification From Face Images Using Local Descriptors

Cited by 26 publications

References 16 publications

Learning Race from Face: A Survey

Learning Race from Face: A Survey

A study on different experimental configurations for age, race, and gender estimation problems

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

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