Fuzzy similarity-based classification method for gender recognition using 3D facial images

This paper presents a novel gender classification method based on geometry features of palm image which is simple, fast, and easy to handle. This gender classification method based on geometry features comprises two main attributes. The first one is feature extraction by image processing. The other one is classification system with polynomial smooth support vector machine (PSSVM). A total of 180 palm images were collected from 30 persons to verify the validity of the proposed gender classification approach and the results are satisfactory with classification rate over 85%. Experimental results demonstrate that our proposed approach is feasible and effective in gender recognition.

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Automatic gender recognition for “in the wild” facial images using convolutional neural networks

2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) ◽

10.1109/iccp.2017.8117018 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sergiu Cosmin Nistor ◽

Alexandra-Cristina Marina ◽

Adrian Sergiu Darabant ◽

Diana Borza

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Gender Recognition ◽

In The Wild ◽

Facial Images

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A Real Time Gender Recognition System Using Facial Images and CNN

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i9.122126 ◽

2019 ◽

Vol 7 (9) ◽

pp. 122-126

Author(s):

Taran Rishit Undru ◽

CVNS Anuradha

Keyword(s):

Real Time ◽

Recognition System ◽

Gender Recognition ◽

Facial Images

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1D embedding multi-category classification methods

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691316400063 ◽

2016 ◽

Vol 14 (02) ◽

pp. 1640006 ◽

Cited By ~ 3

Author(s):

Luoqing Li ◽

Chuanwu Yang ◽

Qiwei Xie

Keyword(s):

Binary Classification ◽

High Dimensional Data ◽

Classification Performance ◽

Classification Method ◽

High Dimensional ◽

Classification Methods ◽

One Dimensional ◽

Interpolation Technique ◽

Facial Images

In this paper, we propose a novel semi-supervised multi-category classification method based on one-dimensional (1D) multi-embedding. Based on the multiple 1D embedding based interpolation technique, we embed the high-dimensional data into several different 1D manifolds and perform binary classification firstly. Then we construct the multi-category classifiers by means of one-versus-rest and one-versus-one strategies separately. A weight strategy is employed in our algorithm for improving the classification performance. The proposed method shows promising results in the classification of handwritten digits and facial images.

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