Deep neural network features for horses identity recognition using multiview horses’ face pattern

Islem Jarraya; Wael Ouarda; Adel M. Alimi

doi:10.1117/12.2269064

17 March 2017 Deep neural network features for horses identity recognition using multiview horses’ face pattern

Islem Jarraya, Wael Ouarda, Adel M. Alimi

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103410B (2017) https://doi.org/10.1117/12.2269064
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

To control the state of horses in the born, breeders needs a monitoring system with a surveillance camera that can identify and distinguish between horses. We proposed in [5] a method of horse’s identification at a distance using the frontal facial biometric modality. Due to the change of views, the face recognition becomes more difficult. In this paper, the number of images used in our THoDBRL’2015 database (Tunisian Horses DataBase of Regim Lab) is augmented by adding other images of other views. Thus, we used front, right and left profile face’s view. Moreover, we suggested an approach for multiview face recognition. First, we proposed to use the Gabor filter for face characterization. Next, due to the augmentation of the number of images, and the large number of Gabor features, we proposed to test the Deep Neural Network with the auto-encoder to obtain the more pertinent features and to reduce the size of features vector. Finally, we performed the proposed approach on our THoDBRL’2015 database and we used the linear SVM for classification.

Citation Download Citation

Islem Jarraya, Wael Ouarda, and Adel M. Alimi "Deep neural network features for horses identity recognition using multiview horses’ face pattern", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410B (17 March 2017); https://doi.org/10.1117/12.2269064

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