Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Abstract Recent progresses in Convolutional Neural Networks (CNNs) and GPUs have
greatly advanced the state-of-the-art performance for face recognition. However, training …

With the increasing popularity of electric vehicles, cable-driven serial manipulators have
been applied in auto-charging processes for electric vehicles. To ensure the safety of the …

The automatic facial expression tracking method has become an emergent topic during the
last few decades. It is a challenging problem that impacts many fields such as virtual reality …

In this article, we propose a hybrid framework for cross-resolution 3D face recognition which
utilizes a Streamed Attention Network (SAN) that combines handcrafted features with …

Deep neural networks have achieved great success in many fields like bioinformatics,
computer vision, automatic machine translation, etc. DCNN plays vital role in face …

Deep learning advanced face recognition to an unprecedented accuracy. However,
understanding how local parts of the face affect the overall recognition performance is still …

快速精准确定牛个体身份对疾病防控, 品种遗传改良, 奶制品和肉制品质量溯源以及改善农业假
保险索赔等方面具有重要意义. 传统的牛个体识别使用诸如烙印, 耳纹, 耳标和无线射频识别等 …

This paper first studies the generalization ability of the convolutional layer as a feature
mapper (CFM) for extracting image features and the classification ability of the multilayer …

The majority of recent face recognition systems are based on Deep Convolutional Neural
Networks (DCNNs). These networks are trained on massive amounts of face images so as to …