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Convolutional neural network (CNN) based face detectors are inefficient in handling faces of diverse scales. They rely on either fitting a large single model to faces across a large scale range or multi-scale testing. Both are computationally expensive. We propose Scale-aware Face Detection (SAFD) to handle scale explicitly using CNN, and achieve better performance with less computation cost. Prior...
Facial expression recognition, which many researchers have put much effort in, is an important portion of affective computing and artificial intelligence. However, human facial expressions change so subtly that recognition accuracy of most traditional approaches largely depend on feature extraction. Meanwhile, deep learning is a hot research topic in the field of machine learning recently, which intends...
One of the major difficulties encountered by current face recognition systems deception in the problems of handling partial face recognition such as varying poses, illumination, light scattering, diffused faces i.e., recognition of faces in random in-depth rotations. The face image differences caused by rotations are often superior to the inter-person differences used in distinctive identities. Initially...
Semi-supervised learning approach is a fusion approach of supervised and unsupervised learning. Semi-supervised approach performs data learning from a limited number of available labelled training images along with a large pool of unlabelled data. Semi-supervised discriminant analysis (SDA) is one of the popular semi-supervised techniques. However, there is room for improvement. SDA resides in the...
In this paper, we propose a new local descriptor based on the approximate radial gradient transform. Initially, the interest points are detected using the difference of box filters. Then, the circular region of certain size around these interest points is represented using the approximate radial gradient transform and hence the descriptor is formed. We have conducted experiments on some of the well...
A person's face provides a lot of information such as age, gender and identity. Faces allow humans to estimate/ classify the age of other persons just by looking at their face. Researchers who carried out work in studying the process of age classification by humans conclude that humans are not so accurate in age classification; hence the possibility of developing facial age classification methods...
In contrast to global image descriptors which compute features directly from the entire image, local descriptors representing the features in small local image patches have proved to be more effective in real world conditions. This paper considers three recent yet popular local descriptors, namely Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features...
This paper addresses the problem of binary classifier learning when the training data is imbalanced, i.e. the samples of the two classes have significantly different cardinality. We investigate two different cost-sensitive approaches in the conditional mutual information (CMI) based weak classifier selection procedure using histogram descriptors. The first method uses CMI for classifier selection,...
Active Shape Model (ASM) is a powerful statistical tool for image interpretation, especially in face alignment. In the standard ASM, local appearances are described by intensity profiles, and the model parameter estimation is based on the assumption that the profiles follow a Gaussian distribution. It suffers from variations of poses, illumination and expressions. In this paper, an improved ASM framework,...
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