Graduate School and Research Center in Digital Sciences

Learned vs. hand-crafted features for pedestrian gender recognition

Antipov, Grigory; Berrani, Sid-Ahmed; Ruchaud, Natacha; Dugelay, Jean-Luc

Research Report RR-15-302

This paper addresses the problem of image features selection for pedestrian gender recognition. Hand-crafted features (such as HOG) are compared with learned features which are obtained by training convolutional neural networks. The comparison is performed on the recently created collection of versatile pedestrian datasets which allows us to evaluate the impact of dataset properties on the performance of features. The study shows that hand-crafted and learned features perform equally well on small-sized homogeneous datasets. However, learned features significantly outperform handcrafted ones in the case of heterogeneous and unfamiliar (unseen) datasets. Our best model which is based on learned features obtains 79% average recognition rate on completely unseen datasets.

Document Bibtex

Title:Learned vs. hand-crafted features for pedestrian gender recognition
Keywords:Pedestrian gender recognition, convolutional neural networks, HOG, image features, supervised learning
Department:Digital Security
Eurecom ref:4546
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Research Report RR-15-302 and is available at :
Bibtex: @techreport{EURECOM+4546, year = {2015}, title = {{L}earned vs. hand-crafted features for pedestrian gender recognition}, author = {{A}ntipov, {G}rigory and {B}errani, {S}id-{A}hmed and {R}uchaud, {N}atacha and {D}ugelay, {J}ean-{L}uc }, number = {EURECOM+4546}, month = {04}, institution = {Eurecom}, url = {},, }
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