The emergence of deep learning techniques brought remarkable breakthroughs to face detection along with the price of a considerable increase in computation. However, with the tremendous increase in images and videos with variations in face scale, appearance, expression, occlusion and pose, traditional face detectors are challenged to detect various "in the wild" faces. In recent decades, researchers have created many typical and efficient face detectors from the Viola-Jones face detector to current CNN-based ones. Many applications including face recognition, facial expression recognition, face tracking and head-pose estimation assume that both the location and the size of faces are known in the image. 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.įace detection is to search all the possible regions for faces in images and locate the faces if there are any. The fusion of CNNAFD and MobileNetV2 constructs the new network CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. CNNAFD ends by stacked fully connected layers which represent a strong classifier. The ANOFS method is used as a training optimizer for the new ANOFS-Conv layer. A new sparse convolutional layer ANOFS-Conv is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). ![]() ![]() CNNAFD used a processed filters based on gradient features and applied with a new way. 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). 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. ![]() Existing detectors are generally based on Convolutional Neural Networks (CNNs) as backbones. This paper focuses on the face detection problem of three popular animal categories that need control such as horses, cats and dogs. Meanwhile LBP method is the fastest among the proposed methods with an average FPS of 25.58 and 33.79. However, it is the slowest when it comes to detecting faces with an average number of frames per second (FPS) of 2.12 and 2.58. It achieves an average detection rate of 99.99% for the DEAP database and 84.23% for the RECOLA database. The experimental results show that CNN based algorithm is more efficient compared to other approaches. Different experiments were conducted to evaluate the performance of these techniques on constraint and spontaneous video sequences from the database for Remote Collaborative and Affective Interactions (RECOLA) and the Database for Emotion Analysis using Physiological Signals (DEAP). ![]() This paper introduces a critical comparison to a variety of face-detection methods, namely, (1) Haar-like cascade, (2) Linear Binary Pattern cascade (LBP), (3) Histogram of Oriented Gradients with Support Vector Machine (HOG) and (4) Convolutional Neural Network based algorithms (CNN) using video sequences rather than static images. Therefore, developing a high, accurate and efficient near-real-time face detection method has become a major concern for both industrial and research communities. Real time face detection techniques are needed in a wide range of fields.
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