This document proposes a face recognition system using multimodal and multi-algorithmic feature fusion of hybrid and Kekre wavelet-based feature vectors. The system first pre-processes hyperspectral face images and applies hybrid wavelet transforms and Kekre's wavelet transform to generate feature vectors. These feature vectors are then analyzed using intra-class and inter-class testing to evaluate metrics like true acceptance rate and true rejection rate. The results show that fusing features from multiple algorithms like hybrid wavelet type I, hybrid wavelet type II and Kekre's wavelet transform provides better performance than individual unimodal systems.

1. Face Recognition by Multimodal
and Multi Algorithmic Feature
Fusion of Hybrid and Kekre
Wavelets based Feature Vectors
Authors
Pallavi P. Vartak
&
Dr. Vinayak A. Bharadi

2. Contents
• Introduction
• Literature Survey
• Proposed System
• Proposed Algorithm
• Results & Discussions

3. Introduction
• Biometrics
Technologies that are automated to make attempts
for conformation of an individuals claimed identity.
• How ?
By comparing a submitted sample to one or more
previously enrolled templates.
• Types
Hand based Biometrics & Face based Biometrics.

4. Introduction contd.
• Classification based on requirement
Unimodal Systems
&
Multimodal Systems

5. Introduction contd.
• Identifying Humans by their faces is the oldest technique
used.
• What is Face Recognition of Hyperspectral Images ?

6. Introduction contd.
• HYPERSPECTRAL IMAGES
• They contain a great number of spectral bands or spectra.
• They can acquire the intrinsic spectral information of the
skin at many delicate wavelengths.
• It has ability to capture distinct personal identification
patterns shaped by their molecular composition that
relates to tissues, blood and structure.
• Can overcome the difficulties faced in traditional face
recognition systems, like variance of face orientation,
light distortion or expressions.

7. Introduction contd.
• THE BIOMETRIC RESEARCH CENTRE AT
HONG KONG POLYTECHNIC UNIVERSITY
• In this research we have used Hyperspectral face database
developed by them which provides us an opportunity to
advance the research in face recognition and compare its
effectiveness. In this existing system individual image
band is used for feature extraction and recognition.

8. Introduction contd.
Illustration of a set of 33 Hyperspectral face bands The Hong Kong
Polytechnic University Hyperspectral Face database (Poly U-HSFD)

9. Literature Survey
• Face biometric belongs to both physiological and behavioral
categories.
• Face has advantage over other biometrics because it is a
natural, non-intrusive, and easy-to-use biometric. [1] ,[9] &
[10].
• Statistical techniques, such as PCA [11], LDA [12], ICA [13]
and Bayes [14] etc., are used to extract low dimensional
features from the intensity image directly for recognition

10. Literature Survey contd.
• Multi resolution Transform such as, Gabor Wavelet Transform, was
used to extract the spatial frequency, spatial locality and orientation
selectivity from faces irrespective of the variations in the expressions,
illumination and pose [18]
• 3 methods are proposed for hyperspectral face recognition, including
whole band (2D)2PCA, single band (2D)2PCA with decision level
fusion, and band subset fusion based (2D)2PCA
• H. B Kekre and V. A Bharadi [19] detailed the concept of hybrid wavelet
transform in interpretation of combining traits of two different
orthogonal transform wavelets to achieve the strength of both the
transform wavelets.

11. Literature Survey contd.
• The hybrid of DCT and DKT gives best results among the
combination of four transforms used for generating hybrid wavelet
transforms.
• Kekre, Sarode and Dhannawat [20] used Kekre’s wavelet combine
images of same object or scene so that the final output image
contains more information such image fusion gave comparatively
better results just closer to best results with an added advantage
wherein it can be used for images of any sizes, not necessarily integer
power of 2

12. Literature Survey contd.
• V. A. Bharadi, P. Mishra and B. Pandya [15] used hyperspectral
images with 33 band are used for generation of feature vector
based on Vector Quantization (VQ) process. Popular VQ
Algorithms like Kekre’s Fast Codebook Generation (KFCG)
Algorithm and Kekre’s Median Codebook Generation (KMCG)
Algorithm are used to generate codebooks. These results clearly
indicated that the security performance index of KMCG and KFCG Front is
better than that of Left, Right, Left + Right, Front + Left + Right. The
PI of KFCG and KMCG Front + Left + Right is better than other feature
vector type.

13. Proposed System

14. Proposed System Block Diagram Description
• Pre-Processing block is used to accept the
hyperspectral face image data. The PolyU
Hyperspectral Face Database [7] is used for
this current research. The database contains
face images each with 33 frequency bands.
These instances of the image are taken at 33
diverse frequencies with the help of
hyperspectral image capturing sensors. In
which Front, Left and Right side face
images are captured. These images are
stored in Hypercube MAT [21] format; they
are also called as ‘Face cubes’.

15. Proposed System Block Diagram Description
• Hybrid Wavelet Transforms are
performed on this data. The Hybrid
Wavelet Type I (HW TI)
Transform, Hybrid Wavelet Type II
(HW TII) Transform and Kekre’s
Wavelet (KW) Transform is used
in order to generate Feature Vector.
This process generates feature
vectors for each user by HWTI,
HWTII and KW.

16. Proposed System Block Diagram Description
• These Feature Vectors are stored in
the database. Further Analyzed by
Intra class testing and Inter class
testing, which results in Genuine and
forgery data sheets. Final step is to
analyze the performance of the
proposed technique for biometric
authentication based on Multi
Instance Fusion and Multi
Algorithmic Fusion for TAR, TRR
will be performed on above feature
vector. Distance between two faces
can be evaluated by evaluating the
Euclidean Distance using KNN
Classifier .

17. Proposed Algorithm.
• Step 1: Start by reading MAT file and its face cubes, this gives a
composite Array for 33 Bands of the Facecubes data.
• Step 2: Next read band data for each image. The total 33 Bands of the
face image are available. These bands of the face image are
taken at 33 different each image is of 180*220 Pixel sizes [22].
Perform Normalization on the data, so that the grey levels are
in-between (0-255).

18. Proposed Algorithm contd..
• Step 3: Then these images are
grouped into eleven sub-bands
of 3 images each. We are
considering 3 components
(F,L and R).Each of which
will be having 4 blocks for 5
levels of decomposition, this
gives the size of the
Featurecount for 33 bands and
240 values from
Components*Blocks
*Blocks*Levels.

19. Proposed Algorithm contd.
• Step 4: This result in feature vectors form HWI, HWII and KW
Transforms for each user.
• Step 5: This feature vector database is used for Intra Class Testing
and Inter Class testing, which generates in Genuine (406
rows and 33 columns) Forgery (5638 rows and 33 columns)
• Step 6: These codebooks are the feature vectors of the hyperspectral
face. In this database Front, Left and Right instances of the
same face are captured.

20. Proposed Algorithm contd.
• If these instances are considered to build a Multi-instance face
recognition system then the 33 columns are grouped into 11 sub
bands (L+R and/or F+L+R) and final set of codebooks is extracted
and stored in the database.
• These instances are then considered for various fusion combinations
of algorithms like HWI+HWII, HWI+KW, HWII+KW and
HWI+HWII+KW are used to build a Multi-Algorithmic Face
Recognition System then same procedure is applied and again the
final set of codebooks is extracted and stored in the database.

21. Results & Discussions.
• The results are discussed in two aspects, first the Multi Instance
Analysis and then the Multi Algorithmic Analysis.
• Evaluation metrics such as True Acceptance Rate (TAR), True
Rejection Rate (TRR), Security Performance Index (SPI) and
Performance Index (PI) are evaluated here for comparison purpose.
Euclidean Distance is calculated evaluation for classification.
• The PolyU HSFD is used for testing for the proposed method.
Some of the subjects used for feature vector extraction, intra & inter
class matching. The feature vectors are evaluated and stored in the
database.
• Security Performance Index (SPI) – This is a new parameter proposed
by Dr. H. B. Kekre [25], this parameter indicates how fast the Equal
Error Rate (EER) is achieved.

22. Results & Discussions contd.
PI
HWI+HWII HWI+KW HWII+KW HWI+HWII+KW
LR FLR LR FLR LR FLR LR FLR
1 73.09 74.33 77.78 77.78 76.8 77.54 72.35 69.63
2 74.57 75.81 77.29 77.29 78.28 76.8 75.56 76.3
3 75.81 76.55 76.3 76.55 77.04 77.54 77.54 78.03
4 75.81 76.3 76.05 76.05 77.04 78.03 84.2 84.94
5 75.81 76.3 76.05 76.05 77.78 78.03 78.77 76.8
6 75.81 76.3 76.05 75.56 77.78 78.03 79.26 79.51
7 75.81 76.3 76.05 75.56 77.78 78.03 79.76 80
8 75.81 76.3 76.05 75.56 77.78 78.03 77.29 82.23
9 75.81 76.3 76.05 75.56 77.78 78.03 81.73 81.73
10 75.81 76.3 76.05 75.56 77.78 78.03 82.97 83.96
11 75.81 76.3 76.05 75.56 77.78 78.03 80.25 81.98
SPI
HWI+HWII HWI+KW HWII+KW HWI+HWII+KW
LR FLR LR FLR LR FLR LR FLR
1 47.62 47.62 34.79 34.79 44.45 40.75 46.16 50
2 45.46 40.91 33.34 34.79 28 29.17 47.37 47.37
3 45.46 45.46 39.14 34.79 34.62 36 37.5 40.91
4 45.46 45.46 39.14 34.79 36 32 64.87 52.95
5 45.46 45.46 39.14 34.79 34.62 36 44.45 44.45
6 45.46 45.46 39.14 34.79 34.62 36 46.16 46.16
7 45.46 45.46 39.14 34.79 34.62 36 40.75 40.75
8 45.46 45.46 39.14 34.79 34.62 36 61.91 57.7
9 45.46 45.46 39.14 34.79 34.62 36 47.62 50
10 45.46 45.46 39.14 34.79 34.62 36 42.31 44
11 45.46 45.46 39.14 34.79 34.62 36 46.43 46.43
Comparison of PI and SPI for algorithms by Multi Algorithmic Analysis.

23. Results & Discussions contd.
• Results clearly state that Multimodal Multi Algorithmic
fusion of all three algorithms used here
(HWI+HWII+KW) gives a better performance when
compared to Unimodal Systems.

24. Thank you ! ! !