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

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Abstract

Nowadays, many applications use biometric systems as a security purpose. These systems use fingerprints, iris, retina, hand geometry, etc. that have unique patterns from person to another. The human face is one of the most important organs that has many physiological characteristics such as the subject gender, race, age, and mood. Determining the gender of the face can reduce the processing time of large-scale face-based systems and may improve the performance. Many studies were proposed for gender recognition, but several were evaluated using the accuracy as a performance metric which is improper for unbalanced data. Further, they used a grayscale color; and extracted features either from the whole image or equally divided blocks, as a grid. In this paper, novel methods are proposed based on statistical features that have the ability to represent the face landmarks. These features are GIST, pyramid histogram of oriented gradients, GIST based on discrete cosine transform and principal component analysis that are extracted using face local regions. The performances are evaluated using area-under-the-curve that is computed from the receiver operating characteristic or ROC curve. At the end, the acquired performance has been compared by two state-of-the-art techniques that shows that the proposed approaches enhance the performance between 1 and 3%, but the number of features is increased.

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Acknowledgements

The authors express their deep gratitude to King Fahd University of Petroleum and Minerals for supporting this research.

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Authors and Affiliations

  1. Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    Ebrahim Al-wajih & Lahouari Ghouti

  2. Computer Science Department, Hodeidah University, 3114, Alduraihimi, Hodeidah, Yemen

    Ebrahim Al-wajih

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  1. Ebrahim Al-wajih
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  2. Lahouari Ghouti
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Correspondence to Ebrahim Al-wajih.

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Al-wajih, E., Ghouti, L. Gender recognition using four statistical feature techniques: a comparative study of performance. Evol. Intel. 12, 633–646 (2019). https://doi.org/10.1007/s12065-019-00264-z

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