08-02-2013, 11:34 AM
Face Recognition
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Face Segmentation/Detection
Before the middle 90’s, the research attention was only focused on single-face segmentation. The approaches included:
Deformable feature-based template
Neural network
Using skin color
During the past ten years, considerable progress has been made in multi-face recognition area, includes:
Example-based learning approach by Sung and Poggio (1994).
The neural network approach by Rowley et al. (1998).
Support vector machine (SVM) by Osuna et al. (1997).
Example-based learning approach (EBL)
Three parts:
The image is divided into many possible-overlapping windows, each window pattern gets classified as either “a face” or “not a face” based on a set of local image measurements.
For each new pattern to be classified, the system computes a set of different measurements between the new pattern and the canonical face model.
A trained classifier identifies the new pattern as “a face” or “not a face”.
Neural network (NN)
Kanade et al. first proposed an NN-based approach in 1996.
Although NN have received significant attention in many research areas, few applications were successful.
LDA/FDA
Face recognition method using LDA/FDA is called the fishface method.
Eigenface use linear PCA. It is not optimal to discrimination for one face class from others.
Fishface method seeks to find a linear transformation to maximize the between-class scatter and minimize the within-class scatter.
Test results demonstrated LDA/FDA is better than eigenface using linear PCA (1997).
Video-based Face Recognition
Three challenges:
Low quality
Small images
Characteristics of face/human objects.
Three advantage:
Allows Provide much more information.
Tracking of face image.
Provides continuity, this allows reuse of classification information from high-quality images in processing low-quality images from a video sequence.
Recent approaches
Most video-based face recognition system has three modules for detection, tracking and recognition.
An access control system using Radial Basis Function (RBS) network was proposed in 1997.
A generic approach based on posterior estimation using sequential Monte Carlo methods was proposed in 2000.
A scheme based on streaming face recognition (SFR) was propose in August 2002.
Summary
Significant achievements have been made. LDA-based methods and NN-based methods are very successful.
FERET and XM2VTS have had a significant impact to the developing of face recognition algorithms.
Challenges still exist, such as pose changing and illumination changing. Face recognition area will remain active for a long time.