Hardware Implementation of Image Recognition System Based on Morphological Associative Memories and Discrete Wavelet Transform

Public services are available to all communities including people with disabilities. One obstacle that impedes persons with disabilities from participating in various community activities and enjoying the various public services available to the community is information and communication barriers. One way to communicate with people with disabilities is with hand gestures. Therefore, the hand gesture technology is needed, in order to facilitate the public to interact with the disability. This study proposes a reliable hand gesture recognition system using the convolutional neural network method. The first step, carried out pre-processing, to separate the foreground and background. Then the foreground is transformed using the discrete wavelet transform (DWT) to take the most significant subband. The last step is image classification with convolutional neural network. The amount of training and test data used are 400 and 100 images repectively, containing five classes namely class A, B, C, # 5, and pointing. This study engendered a hand gesture recognition system that had an accuracy of 100% for dataset A and 90% for dataset B.

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Face Identification Based on Discrete Wavelet Transform and Neural Networks

International Journal of Image and Graphics ◽

10.1142/s0219467819500220 ◽

2019 ◽

Vol 19 (04) ◽

pp. 1950022

Author(s):

Samrat P. Khadilkar ◽

Sunil R. Das ◽

Mansour H. Assaf ◽

Satyendra N. Biswas

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Identification Problem ◽

Recognition System ◽

Face Identification ◽

Discrete Wavelet ◽

Localization Property ◽

Artificial Neural

The subject paper presents implementation of a new automatic face recognition system. To formulate an automated framework for the recognition of human faces is a highly challenging endeavor. The face identification problem is particularly very crucial in the context of today’s rapid emergence of technological advancements with ever expansive requirements. It has also significant relevance in the related engineering disciplines of computer graphics, pattern recognition, psychology, image processing and artificial neural networks. This paper proposes a side-view face authentication approach based on discrete wavelet transform and artificial neural networks for the solution of the problem. A subset determination strategy that expands on the number of training samples and permits protection of the global information is discussed. The authentication technique involves image profile extraction, decomposition of the wavelets, splitting of the subsets and finally neural network verification. The procedure exploits the localization property of the wavelets in both the frequency and spatial domains, while maintaining the generalized properties of the neural networks. The realization strategy of the methodology was executed using MATLAB, demonstrating that the performance of the technique is quite satisfactory.

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A new discrete wavelet transform appropriate for hardware implementation

International Journal of Circuit Theory and Applications ◽

10.1002/cta.2836 ◽

2020 ◽

Vol 48 (9) ◽

pp. 1561-1561

Author(s):

Amin Meshkat ◽

Rasoul Dehghani

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Hardware Implementation ◽

Discrete Wavelet

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VARIANT POSE FACE RECOGNITION USING DISCRETE WAVELET TRANSFORM AND LINEAR REGRESSION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001412560137 ◽

2012 ◽

Vol 26 (06) ◽

pp. 1256013 ◽

Cited By ~ 2

Author(s):

SEYED OMID SHAHDI ◽

SYED A. R. ABU-BAKAR

Keyword(s):

Face Recognition ◽

Wavelet Transform ◽

Linear Regression ◽

Discrete Wavelet Transform ◽

Head Rotation ◽

Recognition System ◽

Discrete Wavelet ◽

Limited Region ◽

The Face ◽

Pose Variation

Face recognition in constraint conditions is no longer a further challenge. However, even the best method is not able to cope with real world situations. In this paper, a robust method is proposed such that the performance of the face recognition system is still highly reliable even if the face undergoes large head rotation. Our proposed method considers local regions from half side of face rather than using the holistic face approach since in the former approach the "linearity" of features within the limited region is somewhat preserved regardless of the pose variation. Discrete wavelet transform is then utilized onto these patches in order to form face feature vectors. We train our recognizer using linear regression algorithm to interpret the relationship between a face vector for a specific pose and its corresponding frontal face feature vector. We demonstrate that our proposed method is able to recognize a non-frontal face with high accuracy even under low-resolution image by relying only on single frontal face in the database.

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