9.913 Pattern Recognition for Vision Class9 - Object Detection and Recognition Bernd Heisele

Outline Object Detection Object Recognition

Object Detection Task: Given an input image, determine if there are objects of a given class in the image and where they are located.

Face Detection System Architecture

Testing

Image Features

ROC for Image Features Gray Gray + Haar Haar Gray + Grad

Positive Training Data

Real vs. Synthetic Real Synthetic

ROC for Classifiers LDA Linear SVM Poly2

Global vs. Components (Whole Face)

Component-based Detection

Some Examples

ROC Component vs. Global About faces 68 people 13 poses 43 illuminations condition CMU PIE database

Training on Faces Positive Facial Negative Non-facial Negative Use the remainder of the face in the negative training set

Training on Faces Red: Trained on facial and non-facial negative set. Blue: Trained only on non-facial negative set.

Pair-wise Biasing Often, many components classify correctly, with only a few errors. Use the pair-wise relative position information from training data to bias the result image.

Pair-wise Biasing Result Images Biased Results

ROC Pair-wise Biasing Red: Trained on facial and non-facial negative set. Blue: Trained only on non-facial negative set. Dashed: Biasing and trained on facial and non-facial negative set.

Pedestrian Detection

Object Recognition Task: Given an image of and object of a particular class identify which exemplar it is.

Recognition System Architecture

Multi-class Classification with SVM Training: N (N-1) / 2 Classification: N - 1 Training: N Classification: N The two different architecture has similar performance!!

Global Approach 1. Detect and extract face 2. Feed gray values of extracted face into N SVMs 3. Classify based on maximum output Each SVM is one vs. all approach

Global Approach with Clustering T1. Partition training images of each person into viewpoint- specific clusters T2. Train a SVM on each cluster. R1. Detect and extract face R2. Feed extracted face to all SVMs R3. Take maximum over all SVM outputs

Component-based Approach 1. Detect face and extract components 2. Combine gray values of components to a feature vector, and feed to the N SVMs 3. Take maximum over all SVM outputs

ROC Component vs. Global Recognition Trained and tested on frontal and rotated faces.