Objective

2

Facial expression recognition is an interesting and challenging problem and affects important applications in many areas

such as human-computer interactions and data-driven animation. Research on 3D facial expression recognition is becoming popular in recent years with the developments of 3D capture technique and face recognition. 3D face data are robust to pose

illumination

and gestures. Such data contain more topological characteristic and geometric information than traditional 2D face data. Thus

3D face data can describe the facial muscle movements caused by expressions. Feature- and model-based methods are the two main types of approaches for 3D facial expression recognition. The former generally performs better than the latter in terms of computational efficiency. Although this method has been successfully applied to facial expression recognition

the existing feature-based methods cannot achieve the desired recognition effects. First

feature-based methods are limited by the issue of extracting features. Some features are extracted using the accurate position of labeled landmarks. Despite their high recognition rates

these features meet many difficulties in practical applications because of landmark location. Other features extracted without labeled landmarks facilitate the achievement of automatic expression recognition. However

these features may have limited information and sometimes tend to fail in recognizing similar expressions. Thus

extracting effective features without labeled landmarks is key for improving 3D facial expression recognition rates by describing the deformation of facial muscles during expressions. Furthermore

proper weights for features can strengthen the identification capabilities of features for expression recognition considering that features of different facial parts have varying degrees of importance. Therefore

we propose an algorithm for 3D facial expression recognition by using weighted local curl patterns. This study aims to extract local curl pattern features and then achieve accurate recognition by computing their weights.

Method

2

The proposed algorithm includes two parts. First

local curl patterns are extracted as highly discriminative features on the basis of the curl vectors of 3D face to represent the facial surface changed by expressions. Curl vectors have been proven to perform better than other features in face representation. The vector direction can be used to represent the spatial location of the 3D surface and the vector length can describe the shape characteristics. Therefore

we construct local curl patterns on the basis of curl vectors. The principle of coding is the same as that of local binary patterns in 2D images. Second

local curl patterns should be assigned with weights considering that different parts of the face have varying sensitivities of expressions. We propose to combine the ICNP(interactive closest normal points) algorithm with minimum projection error algorithm for calculating feature weighs. Finally

weighted local curl pattern features are entered into the classifier

and then the expression class of 3D face is predicted.

Result

2

The algorithm effect is verified by recognizing nine different expressions

including calmness

smile

laugh

surprise

fear

anger

sadness

and disgust

on the basis of the BU-3DFE(Binghamton University 3D facial expression) database

which was developed for 3D facial expression studies. The database contains a total of 100 subjects

including 56 females and 44 males from various ethnic groups and ages. Then

20 of the subjects are selected randomly for training the classifier

and the remaining 80 subjects are selected for recognition. First

the discrimination power of local curl patterns is evaluated via PCA(principal components analysis)-relative entropy. Discrimination power refers to the ratio of inter- and intra-class similarities. The higher the discrimination power

the better the results that will be achieved in recognition. In our experiment

local curl patterns are proven to have the highest discrimination power among common expression features

including normal vectors and shape index. Second

on the basis of the BU-3DFE database

the mean recognition rate of our approach is 89.67%

which is comparable to other methods. The proposed method also achieves low error rates among angry

sad

and disgusting faces

which are often confused in expression recognition. The error rate between angry and sad is 6.26%

and that between angry and disgust is 5.38%.

Conclusion

2

Local curl patterns are proven to extract the features of 3D faces effectively and perform well in expression recognition. The combination of ICNP and minimum projection error algorithms

which is more effective than traditional approaches

can enhance the discrimination power of local curl patterns. Experimental results show that the proposed approach is comparable to state-of-the-art methods in terms of its accuracy and excellent performance

especially for recognizing confusing expressions.