Authors:
Mohamed Selim
1
;
Suraj Sundararajan
1
;
Alain Pagani
2
and
Didier Stricker
3
Affiliations:
1
Technical University Kaiserslautern, Germany
;
2
German Research Center for Artificial Intelligence (DFKI), Germany
;
3
Technical University Kaiserslautern and German Research Center for Artificial Intelligence (DFKI), Germany
Keyword(s):
Gender, Face, Deep Neural Networks, Quality, In the Wild.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
Gender recognition is an important task in the field of facial image analysis. Gender can be detected using
different visual cues, for example gait, physical appearance, and most importantly, the face. Deep learning
has been dominating many classification tasks in the past few years. Gender classification is a binary classification
problem, usually addressed using the facial image. In this work, we present a deep and compact CNN
(GenderCNN) to estimate the gender from a facial image. We also, tackle the illumination and blurriness that
appear in still images and appear more in videos. We use Adaptive Gamma Correction (AGC) to enhance the
contrast and thus, get more details from the facial image. We use AGC as a pre-processing step in gender
classification in still images. In videos, we propose a pipeline that quantifies the blurriness of an image using
a blurriness metric (EMBM), and feeds it to its corresponding GenderCNN that was trained on faces with
similar blurriness. We evaluat
ed our proposed methods on challenging, large, and publicly available datasets,
CelebA, IMDB-WIKI still images datasets and on McGill, and Point and Shoot Challenging (PaSC) videos
datasets. Experiments show that we outperform or in some cases match the state of the art methods.
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