Gender recognition using four statistical feature techniques: a comparative study of performance

Author:

Al-wajih EbrahimORCID,Ghouti Lahouari

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)

Reference45 articles.

1. Shah DH, Shah DJ, Shah DTV (2014) The exploration of face recognition techniques. Int J Appl Innov Eng Manag (IJAIEM) 3:238–246

2. Arigbabu OA, Ahmad SMS, Adnan WAW, Yussof S, Iranmanesh V, Malallah FL (2014) Gender recognition on real world faces based on shape representation and neural network. In: 2014 International conference on computer and information sciences (ICCOINS), pp 1–5. https://doi.org/10.1109/ICCOINS.2014.6868361

3. Golomb BA, Lawrence DT, Sejnowski TJ (1991) SEXNET: a neural network identifies sex from human faces. In: Advances in neural information processing systems, vol 3, pp 572–577

4. Tapia JE, Pérez Flores CA (2013) Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, and shape. IEEE Trans Inf Forensics Secur 8:488–499

5. JafariBarani M, Faez K, Jalili F (2014) Implementation of gabor filters combined with binary features for gender recognition. Int J Electr Comput Eng (IJECE) 4:108–115

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