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Feature Selection and Feature Manifold for Age Estimation

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Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1377))

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Abstract

In recent years, a number of manifold learning techniques have been proposed in the literature to address the age estimation problem. In manifold methods, appearance features are projected onto a discriminant aging subspace and the age estimation is performed on the aging subspace. In these methods the manifold is learn from the gray intensity images. We propose a feature based discriminant manifold learning and feature selection scheme for robust age estimation. This paper also presents an experimental analysis of the manifold learning and feature selection schemes for age estimation. The exact age value is estimated by applying regression on the resultant feature vector. Experimental analysis on a large scale aging database MORPH-II, demonstrate the effectiveness of the proposed scheme.

Supported by Visvesvaraya National Institute of Technology, Nagpur, India.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India

    Shivani Kshatriya, Manisha Sawant & K. M. Bhurchandi

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  1. Shivani Kshatriya
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  2. Manisha Sawant
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  3. K. M. Bhurchandi
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Editors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, India

    Satish Kumar Singh

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Partha Roy

  3. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  4. Indian Institute of Information Technology Allahabad, Prayagraj, India

    P. Nagabhushan

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Kshatriya, S., Sawant, M., Bhurchandi, K.M. (2021). Feature Selection and Feature Manifold for Age Estimation. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_10

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  • DOI: https://doi.org/10.1007/978-981-16-1092-9_10

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