Gender Classification Using Mixture of Experts from Low Resolution Facial Images

2013 ◽  
pp. 49-60 ◽  
Author(s):  
Yomna Safaa El-Din ◽  
Mohamed N. Moustafa ◽  
Hani Mahdi
Keyword(s):  
Gender Classification ◽  
Mixture Of Experts ◽  
Low Resolution ◽  
Facial Images

Gender classification from unaligned facial images using support subspaces

2013 ◽  
Vol 221 ◽  
pp. 98-109 ◽  
Author(s):  
Wen-Sheng Chu ◽  
Chun-Rong Huang ◽  
Chu-Song Chen
Keyword(s):  
Gender Classification ◽  
Facial Images

scholarly journals Gender Classification of Low-Resolution Facial Image Based on Pixel Classifier Boosting

ETRI Journal ◽  
2016 ◽  
Vol 38 (2) ◽  
pp. 347-355 ◽  
Author(s):  
Kyu-Dae Ban ◽  
Jaehong Kim ◽  
Hosub Yoon
Keyword(s):  
Gender Classification ◽  
Facial Image ◽  
Low Resolution

scholarly journals Gender classification using custom convolutional neural networks architecture

2020 ◽  
Vol 10 (6) ◽  
pp. 5758
Author(s):  
Fadhlan Hafizhelmi Kamaru Zaman
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
High Accuracy ◽  
Input Image ◽  
Gender Classification ◽  
Specific Task ◽  
Male And Female ◽  
Computation Efficiency ◽  
The Arts ◽  
Facial Images

Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed Convolutional Neural Network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some popular state-of-the-arts such as GoogleNet and AlexNet. It also employs batch normalization layers which results in better computation efficiency. Based on experiments using publicly available datasets such as LFW, CelebA and IMDB-WIKI datasets, the proposed custom CNN delivered the fastest inference time in all tests, where it needs only 0.92ms to classify 1200 images on GPU, 1.79ms on CPU, and 2.51ms on VPU. The custom CNN also delivers performance on-par with state-of-the-arts and even surpassed these methods in CelebA gender classification where it delivered the best result at 96% accuracy. Moreover, in a more challenging cross-dataset inference, custom CNN trained using CelebA dataset gives the best gender classification accuracy for tests on IMDB and WIKI datasets at 97% and 96% accuracy respectively.

MULTI-VIEW GENDER CLASSIFICATION USING MULTI-RESOLUTION LOCAL BINARY PATTERNS AND SUPPORT VECTOR MACHINES

2007 ◽  
Vol 17 (06) ◽  
pp. 479-487 ◽  
Author(s):  
HUI-CHENG LIAN ◽  
BAO-LIANG LU
Keyword(s):  
Feature Extraction ◽  
Support Vector Machines ◽  
Local Binary Pattern ◽  
Spatial Information ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Gender Classification ◽  
Classification Rate ◽  
Vector Machines ◽  
Facial Images

In this paper, we present a novel method for multi-view gender classification considering both shape and texture information to represent facial images. The face area is divided into small regions from which local binary pattern (LBP) histograms are extracted and concatenated into a single vector efficiently representing a facial image. Following the idea of local binary pattern, we propose a new feature extraction approach called multi-resolution LBP, which can retain both fine and coarse local micro-patterns and spatial information of facial images. The classification tasks in this work are performed by support vector machines (SVMs). The experiments clearly show the superiority of the proposed method over both support gray faces and support Gabor faces on the CAS-PEAL face database. A higher correct classification rate of 96.56% and a higher cross validation average accuracy of 95.78% have been obtained. In addition, the simplicity of the proposed method leads to very fast feature extraction, and the regional histograms and fine-to-coarse description of facial images allow for multi-view gender classification.

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