S
Sid-Ahmed Berrani
Researcher at Orange S.A.
Publications - 77
Citations - 1192
Sid-Ahmed Berrani is an academic researcher from Orange S.A.. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 17, co-authored 75 publications receiving 1071 citations. Previous affiliations of Sid-Ahmed Berrani include Institut de Recherche en Informatique et Systèmes Aléatoires & Dublin City University.
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Journal ArticleDOI
Effective training of convolutional neural networks for face-based gender and age prediction
TL;DR: This work designs the state-of-the-art gender recognition and age estimation models according to three popular benchmarks: LFW, MORPH-II and FG-NET, significantly outperforming the solutions of other participants and winning the ChaLearn Apparent Age Estimation Challenge 2016.
Proceedings ArticleDOI
Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition
TL;DR: The study shows that hand- crafted and learned features perform equally well on small-sized homogeneous datasets, however, learned features significantly outperform hand-crafted ones in the case of heterogeneous and unfamiliar (unseen) datasets.
Journal ArticleDOI
Minimalistic CNN-based ensemble model for gender prediction from face images
TL;DR: The proposed convolutional neural network ensemble model improves the state-of-the-art accuracy of gender recognition from face images on one of the most challenging face image datasets today, LFW (Labeled Faces in the Wild).
Proceedings ArticleDOI
Robust content-based image searches for copyright protection
TL;DR: A novel search method that trades the precision of each individual search for reduced query execution time is proposed, which dramatically accelerates queries and shows the efficiency and the robustness of the proposed scheme.
Proceedings ArticleDOI
Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models
TL;DR: This work integrates a separate "children" VGG-16 network for apparent age estimation of children between 0 and 12 years old in their final solution and wins the 1st place in the ChaLearn LAP competition significantly outperforming the runner-up.