Facial expression recognition has become an increasingly important area of research in recent years. Neural network- based methods have made amazing progress performing recognition-based tasks, winning competitions set up by various data science communities, and achieving high performance on many datasets. Miscellaneous regularization been utilized researchers to help combat over-fitting, reduc...

With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and recent success deep learning techniques in various fields, neural networks have increasingly been leveraged learn discriminative representations for automatic FER. Recent FER systems generally focus on two important issues: overfitting caused by a lack sufficient train...

This paper introduces emotion recognition for facial expression images using Hidden Markov Model (HMM). Firstly, facial expression images are transformed using discrete cosine transformation and feature is extraction; then HMMs of facial expression images are constructed, and the observation vectors are generated using sub-window, hence, process of emotion recognition for facial expression imag...

Facial expression recognition (FER) has good applications in different aspects of day-to-day life. But not yet realized due to unavailability of effective expression recognition techniques. This paper discusses the application of Gabor filter based feature extraction by using feed-forward neural networks (classifier) for recognition of four different facial expressions from still pictures of th...

Tracking human facial expression within a video image has many useful applications, such as surveillance and teleconferencing, etc. Initially, the Active Appearance Model (AAM) was proposed for facial recognition; however, it turns out that the AAM has many advantages as regards continuous facial expression recognition. We have implemented a continuous facial expression recognition system using...