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A New Convolutional Neural Network based on a Saprse Convolutional Layer for Animal Face Detection
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  • Islem Jarraya ,
  • Fatma BenSaid ,
  • Wael Ouarda ,
  • Umapada Pal ,
  • Adel Alimi
Islem Jarraya
National School of Engineers of Sfax-University of Sfax

Corresponding Author:[email protected]

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Fatma BenSaid
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Wael Ouarda
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Umapada Pal
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Adel Alimi
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This paper focuses on the face detection problem of three popular animal cat-egories that need control such as horses, cats and dogs. To be precise, a new Convolutional Neural Network for Animal Face Detection (CNNAFD) is actu-ally investigated using processed filters based on gradient features and applied with a new way. A new convolutional layer is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the newnetwork CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. Our work also introduces a new Tunisian Horse Detection Database (THDD). The proposed detector with the new CNNAFD-MobileNetV2 network achieved an average precision equal to 99.78%, 99% and 98.28% for cats, dogs and horses respectively.