IEEE Transactions on Pattern Analysis and Machine Intelligence

To improve the human-computer interaction (HCI) to be as good as human-human interaction, building an efficient approach for human emotion recognition is required. These emotions could be fused from several modalities such as facial expression, hand gesture, acoustic data, and biophysiological data. In this paper, we address the frame-based perception of the universal human facial expressions (happiness, surprise, anger, disgust, fear, and sadness), with the help of several geometrical features. Unlike many other geometry-based approaches, the frame-based method does not rely on prior knowledge of a person-specific neutral expression; this knowledge is gained through human intervention and not available in real scenarios. Additionally, we provide a method to investigate the performance of the geometry-based approaches under various facial point localization errors. From an evaluation on two public benchmark datasets, we have found that using eight facial points, we can achieve the state-of-the-art recognition rate. However, this state-of-the-art geometry-based approach exploits features derived from 68 facial points and requires prior knowledge of the person-specific neutral expression. The expression recognition rate using geometrical features is adversely affected by the errors in the facial point localization, especially for the expressions with subtle facial deformations.

/pdf/frame-based-facial-expression-recognition-using-geometrical-4wbq3ys7oj.pdf

Frame-Based facial expression recognition using geometrical features

An algorithm is proposed in this paper to solve the facial feature points landmark by machine instead of by hand It includes two steps Firstly, it estimates the initial position of these feature points with the help of Active Shape Models Secondly, it adjusts these initial points by calculating the similarity of the Gabor vector between the initial points and the referenced points Finally, we can label the feature points accurately Experiments show that our algorithm can locate facial feature points efficiently and robustly

An algorithm based on ASM to label the facial feature nodes automatically

Facial feature detection is a well-studied field. Efficient facial feature detection is significant in face analysis based applications, especially on mobile devices. Balance between accuracy and time efficiency is a practical problem in real time applications. This paper aims at proposing a real-time and accurate algorithm for facial feature detection. It is based on the assumption that classifiers may improve performance by limiting searching region. We propose a simplified Active Shape Model (ASM) to speed up such searching process. To ensure accuracy, several facial feature detectors are compared, such as the Adaboost classifiers with the Haar-feature, and the random forest classifiers. Since the simplified ASM provides a good constraint to different facial features, the detected results are promoted as well. We also design multiple experiments to verify our hypothesis by varying searching region. Experiments on MBGC databases prove the effect of the proposed simplified ASM model (sASM).

A Simplified Active Shape Model for Speeding-Up Facial Features Detection

Efficiency of facial feature detection is very crucial in face related applications such as face recognition and reconstruction. Traditional algorithms of high precision are often with expensive computation. In this paper, we propose a fast facial feature detection algorithm with good precision. The basic idea is to combine fast search strategy in the global image and high precision classifier in the local regions. In a global image, we borrow the idea of Active Shape Model (ASM) and utilize the average search template to decrease the search area in images. At each local region, we use trained random forest classifier (RF) to identify the existence of facial feature. An iteration procedure of template adjustment is specially designed to ensure the detection precision. Experiments show the effectiveness of the proposed facial feature detection algorithm.

Zhonghua Zhou

Papers

Active Shape Model with random forest for facial features detection