A review of facial gender recognition

Ng, Choon-Boon; Tay, Yong Haur; Goi, Bok‐Min

doi:10.1007/s10044-015-0499-6

Cited by 53 publications

(46 citation statements)

References 132 publications

(141 reference statements)

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“…As mentioned above, most state-of-the-art GC approaches focus on the facial pattern. This is evidenced by the latest problem surveys [13,14], and recent results in major journals [2,9,10,15,16,17,18].…”

Section: Related Workmentioning

confidence: 98%

Descriptors and regions of interest fusion for in- and cross-database gender classification in the wild

Castrilln-Santana

Lorenzo-Navarro

Ramn-Balmaseda

2017

Image and Vision Computing

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Gender classification (GC) has achieved high accuracy in different experimental evaluations based mostly on inner facial details. However, these results do not generalize well in unrestricted datasets and particularly in cross-database experiments, where the performance drops drastically. In this paper, we analyze the state-of-the-art GC accuracy on three large datasets: MORPH, LFW and GROUPS. We discuss their respective difficulties and bias, concluding that the most challenging and wildest complexity is present in GROUPS. This dataset covers hard conditions such as low resolution imagery and cluttered background. Firstly, we analyze in depth the performance of different descriptors extracted from the face and its local context on this dataset. Selecting the bests and studying their most suitable combination allows us to design a solution that beats any previously published results for GROUPS with the Dago's protocol, reaching an accuracy over 94.2%, reducing the gap with other simpler datasets. The chosen solution based on local descriptors is later evaluated in a cross-database scenario with the three mentioned datasets, and full dataset 5-fold cross validation. The achieved results are compared with a Convolutional Neural Network approach, achieving rather similar marks. Finally, a solution is proposed combining both focuses, exhibiting great complementarity, boosting GC performance to beat previously published results in GC both cross-database, and full in-database evaluations.

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Section: Related Workmentioning

confidence: 98%

Descriptors and regions of interest fusion for in- and cross-database gender classification in the wild

Castrilln-Santana

Lorenzo-Navarro

Ramn-Balmaseda

2017

Image and Vision Computing

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“…Consequently, FERET is known as the most widely used dataset for evaluating gender [17] recognition methods, and for age estimation FG-NET and MORPH [13] has been widely used. …”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…Feature extraction methods for face gender classification and age estimation can be broadly classified into two parts; that are geometric based and appearance based approaches [17].…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…Generally, a classification system consists of face detection, pre-processing, feature extraction and binary classifier. But, in frameworks like neural networks, feature extraction and classification have been integrated as a single module [17].…”

Section: Gender Classification Frameworkmentioning

confidence: 99%

“…food or cell phone) or facial hair [5]. Factors due to the image captusre process are the person's head pose, lighting or illumination and image quality issues like blurring, low resolution and noise present in the image [17].…”

Section: Challengesmentioning

confidence: 99%

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An Analysis on Gender Classification and Age Estimation Approaches

Kaur¹,

Rai²

2017

IJCA

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There has been a growing interest in automatic age and gender classification, as it has become relevant to an increasing amount of applications such as human-computer interaction, surveillance, biometrics, intelligent marketing and many more. Facial age and gender from the face image of a person is one such significant demographic attribute. In this paper, presents a review of automatic facial gender classification and age estimation framework in computer vision. While highlighting the challenges involved during classification of images captured under unconstrained conditions or may be the laborious process of gathering the face images for age estimation, as aging is the uncontrolled and slow process. A comprehensive survey for facial feature extraction methods and face databases for gender and age estimation studied in the past couple of decades is mentioned. Evaluation and result based performance achieved for various face images from different databases has been explained.

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