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Prof. Dr. Didier Stricker

Dr. Alain Pagani

Dr. Gerd Reis

Eric Thil

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Gabriele Bleser
Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal

Sk Aziz Ali

Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov

Jilliam Maria Diaz Barros

Ramy Battrawy
Hammad Butt

Mahdi Chamseddine
Steve Dias da Cruz

Fangwen Shu

Torben Fetzer

Ahmet Firintepe
Sophie Folawiyo

David Michael Fürst
Kamalveerkaur Garewal

Christiano Couto Gava
Leif Eric Goebel

Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi

Jigyasa Singh Katrolia

Andreas Kölsch
Onorina Kovalenko

Stephan Krauß
Paul Lesur

Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal

Mina Ameli

Nareg Minaskan Karabid
Mohammad Minouei

Pramod Murthy

Mathias Musahl

Peter Neigel

Manthan Pancholi
Qinzhuan Qian

Engr. Kumail Raza
Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar

Alexander Schäfer
Pascal Schneider

René Schuster

Mohamed Selim
Lukas Stefan Staecker

Dennis Stumpf

Yongzhi Su

Xiaoying Tan
Yaxu Xie

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