In this work we address the problem of gender recognition from facial images acquired in the wild. This problem is particularly difficult due to the presence of variations in pose, ethnicity, age and image quality. Moreover, we consider the special case in which only a small sample size is available for the training phase. We rely on a feature representation obtained from the well known VGG-Face Deep Convolutional Neural Network (DCNN) and exploit the effectiveness of a sparse-driven sub-dictionary learning strategy which has proven to be able to represent both local and global characteristics of the train and probe faces. Results on the publicly available LFW dataset are provided in order to demonstrate the effectiveness of the proposed method.

Gender recognition in the wild with small sample size : A dictionary learning approach / D'Amelio, A.; Cuculo, V.; Bursic, S.. - 12232:(2020), pp. 162-169. (Intervento presentato al convegno World Congress on Formal Methods tenutosi a Porto nel 2019) [10.1007/978-3-030-54994-7_12].

Gender recognition in the wild with small sample size : A dictionary learning approach

Cuculo V.;
2020

Abstract

In this work we address the problem of gender recognition from facial images acquired in the wild. This problem is particularly difficult due to the presence of variations in pose, ethnicity, age and image quality. Moreover, we consider the special case in which only a small sample size is available for the training phase. We rely on a feature representation obtained from the well known VGG-Face Deep Convolutional Neural Network (DCNN) and exploit the effectiveness of a sparse-driven sub-dictionary learning strategy which has proven to be able to represent both local and global characteristics of the train and probe faces. Results on the publicly available LFW dataset are provided in order to demonstrate the effectiveness of the proposed method.
2020
World Congress on Formal Methods
Porto
2019
12232
162
169
D'Amelio, A.; Cuculo, V.; Bursic, S.
Gender recognition in the wild with small sample size : A dictionary learning approach / D'Amelio, A.; Cuculo, V.; Bursic, S.. - 12232:(2020), pp. 162-169. (Intervento presentato al convegno World Congress on Formal Methods tenutosi a Porto nel 2019) [10.1007/978-3-030-54994-7_12].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1300661
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