Time-Efficient Hybrid Approach for Facial Expression Recognition

Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database

Wavelet based approach for facial expression recognition

Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs) have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs) are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4) wavelet and Coiflet (1) wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database

Finding faces for Gender classification using BPNN AND PCA based recognition

Face Classification system is a computer program which will classify a face into different categories as per certain criterias. We will be training our systems to classify the images into two septum. The first would classify the images based on human or non--human characteristics. The second category of classification would be classifying the human faces as male or female face. We will implement this using Back Propagation Neural Network algorithm. This pre-classification will help in reducing the total time required to recognize an image and hence increasing the overall speed. Face Recognition system is a computer applicaton that identifies a face of a person in a digital image by comparing the face in the image with the facial database of some trained images. This system can recognize images of a person with emotions and expressions different with those in the facial database. We will implement this using a mathematical tool called Principal Component Analysis and Mahalanobis Distance Algorithm. It can be used for security purposes at restricted places by granting access to only authorised persons. It can also be used for criminal identification