Prof. Dr. Didier Stricker

Dr. Alain Pagani

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Gabriele Bleser

Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Bertram Taetz

Dr. Muhammad Zeshan Afzal

Mhd Rashed Al Koutayni

Jilliam Maria Diaz Barros

Mahdi Chamseddine

Steve Dias da Cruz

Ahmet Firintepe

Sophie Folawiyo

David Michael Fürst

Kamalveerkaur Garewal

Christiano Couto Gava

Leif Eric Goebel

Tewodros Amberbir Habtegebrial

Jigyasa Singh Katrolia

Andreas Kölsch

Onorina Kovalenko

Muhammad Jameel Nawaz Malik

Nareg Minaskan Karabid

Mohammad Minouei

Manthan Pancholi

Engr. Kumail Raza

Dr. Nadia Robertini

María Alejandra Sánchez Marín

Dr. Kripasindhu Sarkar

Alexander Schäfer

Pascal Schneider

Lukas Stefan Staecker

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão

Image Quality-Aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild

Image Quality-Aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild
Mohamed Selim, Suraj Sundararajan, Alain Pagani, Didier Stricker
International Conference on Computer Vision Theory and Applications (VISAPP-18), 13th, January 27-29, Funchal, Madeira, Portugal

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 evaluated 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.

Keywords:: Gender, Face, Deep Learning, Quality, In the Wild, CNN

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