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Comparison of Performance of Haar Wavelet Transform on 3D-2D Multi-Sensor Face Images Using Simulation Model

Suranjan Ganguly, Debotosh Bhattacharjee, Mita Nasipuri


Human face has its own unique characteristics that are always considered to be the valid biometric feature for automation of security systems. A dedicated system doesn’t only require accurate algorithm but also it is required to be executed with a minimum span of time. In this literature, authors have stated and demonstrated a wavelet based feature space design mechanism from human faces and also analyzed the performance of execution for thermal, digital and range face images. Thus, the developed algorithm for variation of sensors’ (2D and 3D-especially 2.5D face images) data with considerable execution time for any electronic system can be considered for automation purpose. All these face images have been normalized into the same scope, and then Haar wavelet transform have been implemented for them. The simulation model has been designed in MATLAB-SIMULINK software environment, and it is tested for all these three types of image sources. In addition, an array of investigation with different parameter setups has also been reported. According to the analysis of the implemented model, the minimum time span that is required for Haar wavelet transform of 2D visual (UGC-JU database), 2D visual (Frav3D database), and thermal face image is 9.89998 Sec., 9.789 Sec. and 10.299558 Sec. respectively.  The same algorithm takes 10.9801 Sec for original 2.5D frontal range face image where for rotated face image, the algorithm at first registers to frontal pose and then performs wavelet transform and takes minimum of 20.77084 Sec.

Keywords: 3D face image, 2D face image, Haar wavelet transformation, embedded system, simulation model, 2.5D range image


Cite this Article: Suranjan Ganguly, Debotosh Bhattacharjee, Mita Nasipuri. Comparison of Performance of Haar Wavelet Transform on 3D-2D Multi-Sensor Face Images Using Simulation Model. Research & Reviews: A Journal of Embedded System & Applications (RRJoESA). 2015; 3(1): 11–27p.


Ganguly S, Bhattacharjee D, Nasipuri M. 3D Face Recognition from Range Images Based on Curvature Analysis. ICTACT Journal on Image and Video Processing. 2014; 04(03): 748–753p.

URL: cse 571-11/ftp/biomet/, Accessed 2 Jun 2014.

Seal A, Bhattacharjee D, Nasipuri M, et al. UGC-JU Face Database and its Benchmarking using Linear Regression Classifier. Multimed Tools Appl. 2013. DOI 10.1007/s11042-013-1754-8.

Frav3D Database. URL: http://archive. today/B1WeX, Accessed 20 Jul 2014.

Ganguly S, Bhattacharjee D, Nasipuri M. 2.5D Face Images: Acquisition, Processing and Application. Computer Networks and Security, in the Proceedings of International Conference on Communication and Computing. 2014; 36–44p. ISBN: 9789351072447.

Gonzalez C, Woods RE. Digital Image Processing. 3rd Edn. 2012.

Mian AS, Bennamoun M, Owens R. An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition. IEEE Trans Pattern Anal Machine Intell. 2007; 29(11): 1927–1946p.

Chien JT, Wu CC. Discriminant Wavelet Faces and Nearest Feature Classifiers for Face Recognition. IEEE Trans Pattern Anal Machine Intell. 2012; 24(12): 1644–1649p.

Vinay KB, Shreyas BS. Face Recognition Using Gabor Wavelets. Fortieth Asilomar Conference on Signals, Systems and Computers. 2006; 593–597p. DOI 10.1109/ACSSC.2006.354817.

Farooq WHO, Datta S. Wavelet based Sub-Space Features for Face Recognition. Congress on Image and Signal Processing (CISP). 2008; 426–430p. DOI 10.1109/CISP.2008.618.

Chen F, Wang Z, Xu Z, et al. Facial Expression Recognition Using Wavelet Transform and Neural Network Ensemble. Second International Symposium on Intelligent Information Technology Application. 2008; 871–875p. DOI 10.1109/IITA.2008.24.

Garcia C, Zikos G, Tziritas G. A Wavelet-based Framework for Face Recognition. 1998; 1–7p.

Patil SM, Kasturiwala SB, Dahad SO, et al. Daubechies Wavelet Tool: Application for Human Face Recognition. International Journal of Engineering Science and Technology (IJEST). 2011; 3(3): 2392–2398p. ISSN : 0975-5462

Bhattacharjee D, Seal A, Ganguly S, et al. A Comparative Study of Human Thermal Face Recognition Based on Haar Wavelet Transform and Local Binary Pattern. Hindawi Publishing Corporation, Comput Intell Neurosci. 2012; 2012. DOI 10.1155/2012/261089.

Yand W, Chua CS. Face Recognition from 2D and 3D Images Using 3D Gabor Filters. Image Vision Comput. 2005; 11(23): 1018–1028p.

Rajwade A, Levine MD. Facial Pose from 3D Data. 1–15p. URL: http://www., Accessed 28 Sep 2014.

Ramadan RM, Abdel-Kader RF. 3D Face Compression and Recognition using Spherical Wavelet Parametrization. International Journal of Advanced Computer Science and Applications (IJACSA). 2012; 3(9).

Haar A. ur Theorie der orthogonalen Funktionen Systeme (German). Mathematische Annalen. 2012; 71(1): 38–53p. DOI 10.1007/BF01456927.

Ganguly S, Bhattacharjee D, Nasipuri M. Analyzing the Performance of Haar-Wavelet Transform on Thermal Facial Image Using Matlab-SIMULINK Model. Proc. 1st Intl. Conf. Microelectronics, Circuit and Systems (Micro-2014), 2014; 2: 106–111p. ISBN: 81-85824-46-0.

TFRS using Simulink. URL:, Accessed 26 Sep 2014.

Liang X. Image Binarization using Otsu Method. NLPR-PAL Group, CASIA. 2009.

Digital Image Interpolation. URL:, Accessed 18 Sep 2014.

Ganguly S, Bhattacharjee D, Nasipuri M. Range Face Image Registration using EFRI from 3D Images. In Proc. 3rd Frontiers of Intelligent Computing: Theory and Applications (FICTA). Springer International Publishing. 2015; 323–333p.

3D Geometric Transformation. URL:, Accessed 20 Aug 2014.

Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans Syst, Man, Cybern. 1979; SMC-9(1).

Stollnitz EJ, DeRose TD, Salesin DH. Wavelets for Computer Graphics: A Primer, Part-1. IEEE Comput Graph Appl. 1995; 76–84p.

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