Analysis of Facial Expressions from Video

Images using PCA

Praseeda Lekshmi V., Dr.M.Sasikumar, Naveen S.

Introduction:

In this paper they present a method to analyze facial expression from video images (Video frames are extracted from image sequences.) by focusing on the regions such as eyes, mouth etc. whose geometries are mostly affected by variation in facial expressions. Face regions are extracted from video images. Skin color detection is used for identifying skin region and recognized using Principal Component Analysis (PCA) method. Face images are projected on to a feature space and the weight vectors are compared to get minimum variation.

Problems:

There are several problems in analyzing facial expressions by a computer because expressions are not always universal. It varies with ethnicity.

Approach:

Geometric based method for facial expression analysis from the recognized face. The feature points are located and their coordinates are extracted (geometric coordinates of highly expression reflected areas are extracted for analyzing facial expressions)

Advantages:

• Good performance ratio for both face identification and expression analysis individually.
• The results are still good when do Analysis of Facial Expressions from Video Images using PCA combined the identification and expression parts.
• The computational time and complexity was also very small.

Background & Related Study:

Most face recognition methods fall into two categories: Feature based and Holistic

In feature based method, face recognition relies on localization and detection of facial features such as eyes, nose, mouth and their geometrical relationships.

In holistic approach, entire facial image is encoded into a point on high dimensional space.

Method:

As a first step, images are projected to PCA space for recognizing face regions. After recognizing the face, their system could efficiently identify the expression from the face.

Feature Work:

It is also proposed to extend the work to identify the face and it’s expressions from 3D images.

Conclusion:

In this paper, face recognition and expression classification from video image sequences are explained. Frames were extracted from image sequences. Skin color detection method is applied to detect face regions. A holistic based approach in which whole face was considered for the construction of Eigen space. Our logic performs well for recognition of expressions from face sequences. Use FG-NET consortium database .

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