Facial Feature Extraction Techniques Research Articles

Multi-level graded facial emotion intensity recognition using MCANN for health care

Facial emotion recognition analysis is widely used in various social fields, including Law Enforcement for police interrogation, virtual assistants, hospitals for understanding patients’ expressions, etc. In the field of medical treatment such as psychologically affected patients, patients undergoing difficult surgeries, etc require emotional recognition in real-time. The current emotional analysis employs interest points as landmarks in facial images affected by a few emotions Many researchers have proposed 7 different types of emotions (amusement, anger, disgust, fear, and sadness). In our work, we propose a deep learning-based multi-level graded facial emotions of 21 different types with our proposed facial emotional feature extraction technique called as Deep Facial Action Extraction Units (DFAEU). Then using our Multi-Class Artificial Neural Network (MCANN) architecture the model is trained to classify different emotions. The proposed method makes use of VGG-16 for the analysis of emotion grades. The performance of our model is evaluated using two algorithms Sparse Batch Normalization CNN (SBN-CNN) and CNN with Attention mechanism (ACNN) along with datasets Facial Emotion Recognition Challenge (FERC-2013). Our model outperforms 86.34 percent and 98.6 percent precision.

Facial feature extraction techniques for face detection: A review

Facial feature extraction techniques for face detection: A review

Multiple Active Appearance Model을 이용한 얼굴 특징 추출 기법

Active Appearance Model(AAM) is one of the facial feature extraction techniques. In this paper, we propose the Multiple Active Appearance Model(MAAM). Proposed method uses two AAMs. Each AAM trains using different training parameters. It causes that each AAM has different strong points. One AAM complements the weak points in the other AAM. We performed the facial feature extraction on the 100 images to verify the performance of MAAM. Experiment results show that MAAM gives more accurate results than AAM with less fitting iteration.

Face Recognition using SOM Neural Network with Different Facial Feature Extraction Techniques

Face Recognition using SOM Neural Network with Different Facial Feature Extraction Techniques

Data-Free Prior Model for Facial Action Unit Recognition

Facial action recognition is concerned with recognizing the local facial motions from image or video. In recent years, besides the development of facial feature extraction techniques and classification techniques, prior models have been introduced to capture the dynamic and semantic relationships among facial action units. Previous works have shown that combining the prior models with the image measurements can yield improved performance in AU recognition. Most of these prior models, however, are learned from data, and their performance hence largely depends on both the quality and quantity of the training data. These data-trained prior models cannot generalize well to new databases, where the learned AU relationships are not present. To alleviate this problem, we propose a knowledge-driven prior model for AU recognition, which is learned exclusively from the generic domain knowledge that governs AU behaviors, and no training data are used. Experimental results show that, with no training data but generic domain knowledge, the proposed knowledge-driven model achieves comparable results to the data-driven model for specific database and significantly outperforms the data-driven models when generalizing to new data set.

Human emotion recognition system using optimally designed SVM with different facial feature extraction techniques

This research aims at developing Humanoid Robots that can carry out intellectual conversation with human beings. The first step in this direction is to recognize human emotions by a computer using neural network. In this paper all six universally recognized basic emotions namely angry, disgust, fear, happy, sad and surprise along with neutral one are recognized. Various feature extraction techniques such as Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT), Singular Value Decomposition (SVD) are used to extract the useful features for emotion recognition from facial expressions. Support Vector Machine (SVM) is used for emotion recognition using the extracted facial features and the performance of various feature extraction technique is compared. Authors achieved 100% recognition accuracy on training dataset and 94.29% on cross validation dataset.

Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships

A system that could automatically analyze the facial actions in real time has applications in a wide range of different fields. However, developing such a system is always challenging due to the richness, ambiguity, and the dynamic nature of facial actions. Although a number of research groups attempt to recognize facial action units (AUs) by either improving facial feature extraction techniques, or the AU classification techniques, these methods often recognize AUs or certain AU combinations individually and statically, ignoring the semantic relationships among AUs and the dynamics of AUs. Hence, these approaches cannot always recognize AUs reliably, robustly, and consistently. In this paper, we propose a novel approach that systematically accounts for the relationships among AUs and their temporal evolutions for AU recognition. Specifically, we use a dynamic Bayesian network (DBN) to model the relationships among different AUs. The DBN provides a coherent and unified hierarchical probabilistic framework to represent probabilistic relationships among various AUs and to account for the temporal changes in facial action development. Within our system, robust computer vision techniques are used to obtain AU measurements. And such AU measurements are then applied as evidence to the DBN for inferring various AUs. The experiments show that the integration of AU relationships and AU dynamics with AU measurements yields significant improvement of AU recognition, especially for spontaneous facial expressions and under more realistic environment including illumination variation, face pose variation, and occlusion.

Facial Feature Extraction Method Based on Coefficients of Variances

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two popular feature extraction techniques in statistical pattern recognition field. Due to small sample size problem LDA cannot be directly applied to appearance-based face recognition tasks. As a consequence, a lot of LDA-based facial feature extraction techniques are proposed to deal with the problem one after the other. Nullspace Method is one of the most effective methods among them. The Nullspace Method tries to find a set of discriminant vectors which maximize the between-class scatter in the null space of the within-class scatter matrix. The calculation of its discriminant vectors will involve performing singular value decomposition on a high-dimensional matrix. It is generally memory- and time-consuming. Borrowing the key idea in Nullspace method and the concept of coefficient of variance in statistical analysis we present a novel facial feature extraction method, i.e., Discriminant based on Coefficient of Variance (DCV) in this paper. Experimental results performed on the FERET and AR face image databases demonstrate that DCV is a promising technique in comparison with Eigenfaces, Nullspace Method, and other state-of-the-art facial feature extraction methods.

Fast features for face authentication under illumination direction changes

In this letter we propose a facial feature extraction technique which utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients obtained from horizontally and vertically neighbouring blocks. Face authentication results on the VidTIMIT database suggest that the proposed feature set is superior (in terms of robustness to illumination changes and discrimination ability) to features extracted using four popular methods: Principal Component Analysis (PCA), PCA with histogram equalization pre-processing, 2D DCT and 2D Gabor wavelets; the results also suggest that histogram equalization pre-processing increases the error rate and offers no help against illumination changes. Moreover, the proposed feature set is over 80 times faster to compute than features based on Gabor wavelets. Further experiments on the Weizmann database also show that the proposed approach is more robust than 2D Gabor wavelets and 2D DCT coefficients.