FACIAL EXPRESSION RECOGNITION SYSTEM USING

WEIGHT-BASED APPROACH

Srinivasa K G, Inchara Shivalingaiah, Lisa Gracias, and Nischit Ranganath

M.S Ramaiah Institute of Technology, India

http://www.msrit.edu

Introduction:

This paper present a method to identify the facial expressions of a user by processing images taken from a webcam. This is done by passing the image through 3 stages -face detection, feature extraction, and expression recognition. Face detection is accomplished by implementing the concept of Haar-like features to prioritize reduction in detection time over accuracy.

Problems:

• Presence or absence of structural components like facial hair, glasses, etc., affects the accuracy of detection.
• The orientation of the face affects the feature detection. The face must not be tilted, and must be directed straight at the camera.
• Webcam images captured under improper lighting.

Approach:

Facial features are extracted from their respective regions of interest using Gabor filters and classified based on facial ratios and symmetry. They plotted a minimal number of facial points (12 points) and compared the positions of these points on the neutral expression image and the current expression image. This allowed them to recognize 8 expressions based on the facial movements made as opposed to identifying just the 6 basic expressions as done by research papers previously.

Strength:

They were able to reach an accuracy level of approximately 92% for expression recognition when tested with the JAFFE database. This is a considerably high performance when taking into account the simplicity of the algorithm involved.

Functional Requirements:

• Face localization: To detect the face present in an image taken from the webcam.
• Region of Interest Detection: To divide the face into regions of interest for feature extraction in each region.
• Feature Extraction: To detect the features present in each region of interest.
• Feature Classification: To classify the features detected based on their relative and absolute distances on the face.
• Expression Recognition: To identify the expression made based on the feature movements.

Method:

• Use 3 algorithms to Face Detection, Feature Extraction & Expression Recognition.
•  Functional architecture
•  Face Detection: The input image is scanned across location and scale. At each location, an independent decision is made regarding the presence of a face.
•  Region of Interest (ROI) Detection: Determine the ROI for each point, that is, to define a large region which contains the point that we want to detect.
• Feature Extraction: Predict whether the current point represents a certain facial point or not.
• Feature Classification: Detect correct feature for particular feature point.
•  Expression Recognition: A database of 50 people making 8 expressions

Future work:

 Investigate effects of using a reduced number of features for classification. Also, we plan to conduct extensive experimental studies using other publicly available face databases.

Download Research Paper:

https://drive.google.com/file/d/0B3VmCeqDC7SDSzZxMXQzLVJieFE/view?usp=sharing

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 .

Download Research Paper : https://drive.google.com/file/d/0B3VmCeqDC7SDdTE4LXVZUDZfZ3c/view?usp=sharing