The concept of intelligent visual identification of abnormal human activity has raised the
standards of surveillance systems, situation cognizance, homeland safety and smart …

In recent years, convolutional neural networks have proven to be a highly efficient approach
for face recognition. In this paper, we develop such a framework to learn a robust face …

Performance of face detection and recognition is affected and damaged because occlusion
often leads to missed detection. To reduce the recognition accuracy caused by facial …

In the vehicle type classification area, the necessity to improve classification performance
across traffic surveillance cameras has garnered attention in research especially on high …

Deep learning networks have established themselves as a promising model for face
recognition. Their success is attributed towards multiple processing layers in order to learn …

Automatic face recognition systems have achieved impressive performance in the last
decade, thanks to the advances of deep learning techniques. However, these methods are …

Chinese herbal medicine is an important part of traditional Chinese medicine (TCM). With
developing of traditional Chinese medicine, the usage of Chinese herbal medicine is …

Support vector machines are widely utilised in the field of Face Recognition (FR) but it
suffers from the drawback of high-computational time. In proposed work, new active set …

Different convolutional neural networks (CNNs) may learn different levels of discriminative
features to represent the raw face data. To enhance the discrimination of deeply learned …

Image recognition is the reference of machine vision, is the ability of software to identify
objects, peoples, places, actions and writing in images. Deep learning is the core field of …