A new convolutional neural network based on a sparse convolutional layer for animal face detection

This paper focuses on the face detection problem of three popular animal categories that need control such as horses, cats and dogs. Existing detectors are generally based on Convolutional Neural Networks (CNNs) as backbones. CNNs are strong and fascinating classification tools but present some weak points such as the big number of layers and parameters, require a huge dataset and ignore the relationship between image parts. To be precise, to deal with these problems, this paper contributes to present a new Convolutional Neural Network for Animal Face Detection (CNNAFD), a new backbone CNNAFD-MobileNetV2 for animal face detection and a new Tunisian Horse Detection Database (THDD). CNNAFD used a processed filters based on gradient features and applied with a new way. A new sparse convolutional layer ANOFS-Conv is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). The ANOFS method is used as a training optimizer for the new ANOFS-Conv layer. CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the new network CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. The proposed detector with the new CNNAFD-MobileNetV2 network provides effective results and proves to be competitive with the detectors of the related works with an Average Precision equal to 98.28%, 99.78%, 99.00% and 92.86% on the THDD, Cat Database, Stanford Dogs Dataset and Oxford-IIIT Pet Dataset respectively.