1) The document describes the design of a visual object detection framework capable of rapidly processing images while achieving high detection rates. It begins with a 2D face detection system and extends it to handle colored, multi-pose and 3D images. 2) Key aspects of the system include using Viola-Jones for initial 2D frontal face detection, extracting 3D models from video using structure from motion, and generating frontal views to recognize faces. 3) Experimental results show about a 40% improvement in matching performance by using the 3D models compared to non-frontal 2D frames. The system achieved accuracy comparable to other leading systems and has applications in medical imaging, surveillance, and license plate recognition.

1. Design of a Pattern Recognition and Analysis System
for 3-D Imaging
Sahil Bhatia
Undergraduate Student, Delhi Technological University
INTRODUCTION EXTENSION OF SYSTEM FOR 3-D IMAGES
Humans have an extremely well developed sense of detecting, recognizing and analyzing patterns around them. Extending 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint
this ability to machines has proved to be a very challenging task. In this project, a visual object detection framework was recognition as they depend upon geometry of rigid features on the face. This avoids such pitfalls of 2D face
developed that is capable of processing images extremely rapidly while achieving high detection rates. First, a face detection recognition algorithms as change in lighting, different facial expressions, make-up and head orientation.
system for 2D, frontal, upright and grayscale images was constructed, which was further extended for colored, multi-pose
and 3D imaging. The system was tested successfully over a large database of standard images and gave results comparable to
best similar systems available. In this project, 3-D face recognition is carried on a real time video stream. Video streams are generally of low resolution and
contain mostly non-frontal faces. This drawback is overcome by using 3D models.
The proposed system consists of three stages:
PROBLEM ANALYSIS (i) reconstructing a 3D face model from multiple non-frontal frames in a video
(ii) generating a frontal view from the derived 3D model
Face recognition is the problem of detecting the location and size of face images in a digital image. It is a challenging
problem due to great variation in pose, expressions, ambient lighting conditions, occlusion and orientation in a data image (iii) using the Viola Jones 2D face recognition engine to recognize the synthesized frontal view
and it has not been completely solved yet.
METHODS OF SOLUTION
Initially, for face detection, grayscale and frontal images were considered as the input problem set and a facial recognition
system for these 2-D images was implemented using Viola -Jones method. The Viola-Jones method, given in year 2001, is
world’s first real-time face detection system. The method consists of two stages as mentioned below: Training and Face
Detection. For training, the CMU+MIT standard database of facial images was used.
STAGE 1:
Figure: Structure from motion (SfM) method for producing 3D models and synthesis of frontal view from 2D models
Training
Structure from Motion
1. 1 Creation of 1.2 Calculation of 1.3 AdaBoosting of Obtaining a 3D face model from a sequence of 2D images is an active research problem. Morphable model (MM),
dataset feature intensity weak classifiers stereography, and Structure from Motion (SfM) are well known methods in 3D face model construction from 2D images or
• CMU+MIT Dataset values • Update weights of video.
• 4547- Face images, 2529 • Using rectangular features, misclassified classifiers In this project, SFM was used for producing a 3D model from a monocular video stream. SFM consists of four main elements:
Non Face images calculate the intensity • A linear combination of acquiring 2D video data, identification of points track and tracking those points over time to produce motion tracks. Finally,
• Each of size 19x19 pixels in values of images in dataset weak classifier works as a
strong classifier the motion tracks are converted into a viable 3D model using 3D factorization. 72 facial feature points that outline the eyes,
Grayscale • Calculate Training Error:
Total, Negative and Positive
eyebrows, nose, mouth and facial boundary were used.
Errors
IMPLEMENTATION AND RESULTS
The algorithms for face recognition were implemented on MATLAB. The ROC (Receiver operating characteristic) curves of
both the implementations: 2-D and 3-D are shown below.
For figure 1) As the detection rate increases, number of false positives also increase.
For figure 2) Best accuracy is achieved, as expected, by the frontal video frames, followed by Non-frontal video frames with
3D modeling, followed by Non-frontal 2D video frames
Figure: Sample images Figure: Rectangular features Figure: AdaBoost algorithm
in CMU+MIT dataset
STAGE 2: Face
Detection
2. 1 Image acquisition and 2.2 Calculation of feature 2.3 Removal of multiple
pre-processing intensity values face detections
• Image acquisition is done • A sub-window is passed over • Mainly four kinds of regions in a Figure: ROC plot for Viola-Jones method Figure: ROC plot for frontal, non-frontal
through a normal webcam entire image and the calculated face: eye region, nose region,
feature values are compared mouth region and cheek region. of face detection for 2D, frontal images video frames with and without 3D modeling
• On-line image input by
triggering a video every 1 with corresponding values of • A true face will be detected
second and it is rescaled classifiers starting from first multiple times . The proposed final scheme has been tested on CMU’s Face In Action (FIA) video database with 221 subjects. Experimental
cascade • Detected sub-windows are
• It is also converted into
post-processed in order to
results show a 40% improvement in matching performance as a result of using the 3D models.
grayscale and variance • If image passes through all 38
normalized :Mean=0, cascaded classifiers, a rectangle combine overlapping detections
Variance=1 is drawn at that location to into a single detection.
indicate a face APPLICATIONS OF THE CURRENT SYSTEM
Medical Image Analysis 3-D Television Crowd Surveillance License Plate Detection
Figure: Detection of four Figure- Left ventricle detection results Figure- A camera is used to detect if there Figure- Video quality in crowd and Recognition
Figure: A typical input image Figure: A cascaded classifier. kinds of regions and
on 2D MRI images. Red boxes show the are users wearing special 3D viewing
glasses. Based on detection, display
surveillance is poor and there is a
great variety in pose and lighting. It is
Figure-Video stream in real-time. System
ground truth, while cyan boxes show consists of a detection and a character
which has been variance Green circles are faces and removal of multiple faces. the detection results. automatically switches between 2D and somehow compensated by using
continuous feature detection
recognition module. Detector is based on
3D viewing modes. AdaBoost
normalized red are non faces
MODIFICATIONS IN THE VIOLA-JONES METHOD CONCLUSION AND FUTURE WORK
To improve the initial method of face detection, three additional properties were added in the system: Skin colour, Multi- A method of pattern recognition and analysis for human face recognition was successfully developed and tested. It was
View face detection and Cost-sensitive AdaBoost algorithm. This increased both the accuracy and speed of the detector.
shown that the speed and accuracy of the system was greatly enhanced by getting additional information through skin
color, use of cost effective AdaBoost and by construction of 3D models from multi-view 2D images. Future work can include
use of Support Vector Machines (SVM) and Neural Networks to further enhance the system.
REFERENCES
1. Paul Viola, Michael J. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision 57(2), 2004.
Figure: Binary images after 2. Unsang Park, Anil K. Jain, 3D Model-Based Face Recognition in Video, 2nd International Conference on Biometrics, Seoul,
Figure: Sample Multi-View image
skin binarization binarization
Figure: Skin Figure: Sample input for multi-view Figure: ROC curve of naïve and CS AdaBoost Korea, 2007
Cost-Sensitive AdaBoost 3. Deng Peng, Pei Mingtao, Multi-view Face Detection Based on AdaBoost and Skin Color, First International Conference on
Skin Colour Multi-view face detection
Intelligent Networks and Intelligent Systems
1) Skin colour is an important feature 1) Two types of pose variations are algorithm
of human face used to detect faces considered: non-frontal faces- rotated 1) Two main differences between CS-
faster.
2) All the possible face regions are
out of image plane, non-upright faces-
rotated in the image plane
AdaBoost algorithm and the naive
AdaBoost are: AUTHOR CONTACT DETAILS
extracted by skin colour. As much area 2) Different detectors are built for (I) unequal initial weights are given to
of the image is excluded by the skin- different views of face. A decision tree each training sample according to its Sahil Bhatia
color information, speed of the is trained to determine viewpoint class misclassification cost
algorithm is improved greatly. for a given window of image being Third Year Undergraduate
examined. (2) the weights are updated
3) A pixel in converted YCbCr image is separately for positives and negatives Department of Applied Physics
considered as a skin-pixel if its Cb and 3) Appropriate detector for that at each boosting step
Cr values satisfy following equation: viewpoint can then be run instead of Delhi Technological University
running all detectors on all windows. 2) Due to more effectively focus on
137<Cr<177, 77<Cb<111, face samples, it achieves robust and New Delhi, India
190<Cb+Cr<215 high detection rate with modest false Email Id: sahil.bhatia@dtu.co.in
alarm rate
Contact Number: +91 965 062 6665
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