1. Field of the Disclosure
The present disclosure relates to an emotion recognition method and system thereof. And more particularly, to an emotion recognition algorithm capable of assigning different weights to at least two feature sets of different types based on their respectively recognition reliability while making an evaluation according to the recognition reliability to select feature sets of higher weight among those weighted feature sets to be used for classification, and moreover, it is capable of using a rapid calculation means to train and adjust hyperplanes established by Support Vector Machine (SVM) to be used as a learning process for enabling the adjusted hyperplanes to be used for identifying new and unidentified feature sets accurately.
2. Description of Related Art
For enabling a robot to interact with a human and associate its behaviors with the interaction, it is necessary for the robot to have a reliable human-machine interface that is capable of perceiving its surrounding environment and recognizing inputs from human, and thus basing upon the interaction, to perform desired tasks in unstructured environments without continuous human guidance. In a real world, emotion plays a significant role in rational actions in human communication. Given the potential and importance of emotions, in recent years, there has been growing interest in the study of emotions to improve the capabilities of current human-robot interaction. A robot that can respond to human emotions and act correspondingly is no longer an ice-cold machine, but a partner that can exhibit comprehensible behaviors and is entertaining to interact with. Thus, robotic pets with emotion recognition capability are just like real pets, which are capable of providing companionship and comfort in a nature manner, but without the moral responsibilities involved in caring a real animal.
For facilitating nature interactions between robots and human beings, most robots are designed with emotion recognition system so as to respond to human emotions and act corresponding thereto in an autonomous manner. Most of the emotion recognition methods current available can receive only one type of input from human being for emotion recognition, that is, they are programmed to perform either in a speech recognition mode or a facial expression recognition mode. One such research is a multi-level facial image recognition method disclosed in U.S. Pat. No. 6,697,504, entitled “Method of Multi-level Facial Image Recognition and System Using the Same”. The abovementioned method applies a quadrature mirror filter to decompose an image into at least two sub-images of different resolution. These decomposed sub-images pass through self-organizing map neural networks for performing non-supervisory classification learning. In a test stage, the recognition process is performed from sub-images having a lower resolution. If the image can not be identified in this low resolution, the possible candidates are further recognized in a higher level of resolution. Another such research is a facial verification system disclosed in U.S. Pat. No. 6,681,032, entitled “Real-Time Facial Recognition and Verification System”. The abovementioned system is capable of acquiring, processing and comparing an image with a stored image to determine if a match exists. In particular, the system employs a motion detection stage, blob stage and a flesh tone color matching stage at the input to localize a region of interest (ROI). The ROI is then processed by the system to locate the head, and then the eyes, in the image by employing a series of templates, such as eigen templates. The system then thresholds the resultant eigen image to determine if the acquired image matches a pre-stored image.
In addition, a facial detection system is disclosed in U.S. Pat. No. 6,689,709, which provides a method for detecting neutral expressionless faces in images and video, if neutral faces are present in the image or video. The abovementioned system comprises: an image acquisition unit; a face detector, capable of receiving input from the image acquisition unit for detecting one or more face sub-images of one or more faces in the image; a characteristic point detector, for receiving input from the face detector to be use for estimating one or more characteristic facial features as characteristic points in each detected face sub-image; a facial feature detector, for detecting one or more contours of one or more facial components; a facial feature analyzer, capable of determining a mouth shape of a mouth from the contour of the mouth and creating a representation of the mouth shape, the mouth being one of the facial components; and a face classification unit, for classifying the representation into one of a neutral class and a non-neutral class. It is noted that the face classification unit can be a neural network classifier or a nearest neighbor classifier. Moreover, a face recognition method disclosed in U.S. Pub. No. 2005102246, in which first faces in an image are detected by AdaBoost algorithm, and then face features of the detected faces are identified by the use of Gabor filter so that the identified face features are fed to a classifier employing support vector machine to be used for facial expression recognition. It is known that most of the emotion recognition studies in Taiwan are focused in the filed of face detection, such as those disclosed in TW Pat. No. 505892 and 420939.