The Facial Action Coding System (FACS) is an objective method for quantifying facial movement in terms of 44 component actions, i.e. Action Units (AUs). This system is widely used in behavioral investigations of emotion, cognitive process and social interaction. Highly trained human experts (FACS coders) presently perform the coding. This paper presents a system that can automatically recognize...

The characteristics of human silhouette shape can be used for action recognition and classification. In this paper, a novel feature extraction method the silhouette-based classification actions in videos is proposed. proposed based on polygonization images coding. Since conventional generation methods do not satisfy integrity silhouettes, Yolact++ modified as generator. Our innovative approach ...

Feature extraction on the face plays an important role in applications of model based coding and human face recognition. Traditionally, eyes and mouth are considered to be the most significant features contributing to different facial expressions. However, detecting and tracking the nose shape is non-trivial, and plays an equally important role as eyes and mouth for model based coding, especial...

Recently, the sparse coding based codebook learning and local feature encoding have been widely used for image classification. The sparse coding model actually assumes the reconstruction error follows Gaussian or Laplacian distribution, which may not be accurate enough. Besides, the ignorance of spatial information during local feature encoding process also hinders the final image classificatio...

Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It can gain better representation for the histopathological image than only using coding network. The main process is that training a deep convolutiona...

Local spatio-temporal features are popular in the human action recognition task. In practice, they are usually coupled with a feature encoding approach, which helps to obtain the video-level vector representations that can be used in learning and recognition. In this paper, we present an efficient local feature encoding approach, which is called Approximate Sparse Coding (ASC). ASC computes the...