local binary pattern

The present study aims to assess a combined method for face detection and gender recognition using deep learning by evaluating the shortcomings of face detection methods accurately. Deep learning algorithms can learn high-level features and have attracted a lot of attention for use in the field of machine vision. The present study names a hybrid method called Hyper-Yolo-face and utilizes a clear image using deep Convolution Neural Networks (CNNs), Yolo algorithm, and local binary patterns (LBPs) to identify the face and recognize the gender. Reducing the number of parameters is regarded as an extremely important challenge in deep networks in terms of memory consumption and the amount of computing in the network. The proposed method is based on the AlexNet model and generalization in the loss function of version 3 of the Yolo algorithm, which leads to improved precision. The present study focuses on applying small filters in transfer learning and fine-tuning network layers and using a new regression loss function in the Yolo algorithm to make it more appropriate for multiscale face detection. The face images are detected and cut by the presented Yolo in the proposed method. Then, an LBP operator is applied so that richer information and images enter the AlexNet network to estimate other parameters including gender recognition. Based on the experiments on the AFLW, FDDB, and PASCAL datasets, the proposed method improves recognition precision significantly.

Ensuring the production of non-defect high-quality tires is an essential part of the tire industry. X-ray inspection is one of the best methods to detect tire defects. In this paper, a new approach has been presented for detecting tire defects in X-ray images based on an entropy filter, the extraction of texture properties of patches by Local Binary Pattern, and, finally, the classification of defects using the Support Vector Machine method. In the proposed method, an entropy filter was first applied to the input. The parts of the image with different patterns were then selected as candidate regions and these regions were classified by the patch classifier. All the defects were detected and classified and, finally, the efficiency of the algorithm was evaluated. By applying this algorithm to the dataset the best performance was obtained by the LBP descriptor and the linear SVM classifier with 98\% defect location accuracy and 97\% defect detection accuracy were achieved. In order to analyze the performance, used the deep model as a classifier, thus demonstrating that the deep model has a high capability for learning complex patterns. This proposed method is sensitive to local texture and could well describe texture information, which is appropriate for most kinds of tire defects.