Hand Recognition using Geometric Classifiers Yaroslav Bulatov Sachin Jambawalikar Piyush Kumar Saurabh Sethia 12/9/2018

Why hand recognition? Biometric = nice Cheap Privacy concerns 12/9/2018

Previous Work Jain, Ross and Pankanti, 1999 Jain and Duta, 1999 Sanchez-Reillo et.al., 2000 Oden et.al., 2001 12/9/2018

Methodology Acquire hand images Extract geometric features Classify hands based on the extracted feature vector 12/9/2018

Data Acquisition Use Scanner Flexible Placement No pegs or lighting setup 12/9/2018

Extracting Features Finger lengths, widths, maximum inscribed circles in critical areas. 12/9/2018

Stages of Extraction Thresholding Smoothing Boundary Finding Curvature Graph Distance Transform 12/9/2018

Thresholding 12/9/2018

Thresholding 12/9/2018

Thresholding 12/9/2018

Thresholding Method Convert to CIELAB Use Adaptive Thresholding on B channel 12/9/2018

CIEL*A*B 12/9/2018

Adaptive Thresholding 12/9/2018

12/9/2018

Smoothing Minkowski Smoothing Use round structuring element to perform erosion followed by dilation. 12/9/2018

Before Smoothing 12/9/2018

After Smoothing 12/9/2018

Boundary Finding Find rightmost point Follow boundary pixels CCW At each boundary pixel, scan directions CW, and pick the first available 12/9/2018

Boundary Finding 12/9/2018

Curvature Finding Need to locate feature points (ie fingertips, creases between fingers) Look at curvature graph 12/9/2018

Curvature Finding 12/9/2018

Curvature Finding 12/9/2018

Curvature Finding 12/9/2018

Distance Transform Even after smoothing, some boundary noise present, due to different hand placements Need feature not sensitive to this noise Maximum inscribed circle is one such feature 12/9/2018

Distance Transform Assigns distance to the boundary to each of the point Exact EDT computed in linear time using algorithm by Maurer. 12/9/2018

Distance Transform 12/9/2018

Distance Transform The point with the highest value in the EDT is the center of inscribed circle The largest EDT value is the radius of largest inscribed circle. 12/9/2018

Distance Transform Inscribe circles into fingers by cutting off the finger in question 12/9/2018

12/9/2018

Classification Extract 30 features from each hand image. Given a query hand image, identify which person it belong to 12/9/2018

Nearest Box Classifier Finds the bounding box of each training set. Given a query feature vector, classifies it to the nearest box. 12/9/2018

Minimum Enclosing Ball Classifier (MEB) After 1d, 2d, 3d, …, high d.. What next? 12/9/2018

Minimum Enclosing Ball Classifier (MEB) 1-center in ∞ dimensions!!! 12/9/2018

1-Center in 2002 Badoiu, Har-Peled, Indyk 2002 Ben-Hur et.al. 2002 Badoiu, Clarkson 2002 Kumar, Mitchell, Yildirim 2002 Har-Peled, Varadarajan 2002 12/9/2018

2-Class Classification Given : Two sets of points and determine if belongs to P or Q 12/9/2018

2-Class Classification 12/9/2018

Gaussian Kernel Map P, Q to F using the Gaussian kernel. F 12/9/2018

Classification Algorithm Compute MEB of dim(F ) Here cannot be computed explicitly. Use centers of MEB( ), MEB( ) to determine a separating hyperplane in F 12/9/2018

1-center in ∞ dimensions 12/9/2018

A Projection of H 12/9/2018

A Projection of H 12/9/2018

A Projection of H 12/9/2018

A Projection of H 12/9/2018

Hand Outline Recognition Used Voronoi diagrams in F to extend the idea of 2 class classification. Feature space lived in 30 dimensions. Outlier removal using modified Mahalanobis distance. Compares well with Support Vector Machines for our application (Beats it!) 12/9/2018

Verification Nearest Box MEB 12/9/2018

Identification Nearest Box MEB 12/9/2018

Previous Experimental results Jain et. al. do verification using 500 images. FAR=2%, FRR=15% Jain, Duta do verification using 353 images. FAR=2%, FRR=3.5% Sanchez-Reillo et.al. do verification and classification using 200 images. Error rates: Classification 3%, Verification 10%. Oden et.al. do verification and classification. Error rates: Verification 10%, Classification 15%. 12/9/2018

Misclassification Rates Training Set Size Nearest Box MEB SVM 3 2.81 1.53 4.77 4 1.74 .94 .93 5 1.72 .39 1.93 12/9/2018

Future Work Testing our classifier on standard datasets. Using weighted Voronoi diagram for multiclass classification. Practical provable outlier removal in F 12/9/2018