Non-uniform patch based face recognition via 2D-DWT

Huang, Zheng-Hai; Li, Wenjuan; Shang, Jian Ku; Wang, Jun; Zhang, Ting

doi:10.1016/j.imavis.2014.12.005

Cited by 42 publications

(8 citation statements)

References 28 publications

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“…Following the work involved in this stage: Based on each video frame. I detected faces based on Haarlike features [13] by using Viola-jones method that can detect multiple faces at once. They converted from RGB to gray-scale converted and after this resized into a normalized form by using histogram process equalization.…”

Section: B Processing Before Recognition (Pre-processing)mentioning

confidence: 99%

Education System re-engineering with AI (artificial intelligence) for Quality Im-provements with proposed model

Muzammul

2019

ADCAIJ

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Re-engineering (RE) of existing educational institutions (EI) with adoption of latest technology trends (LTT) in form of artificial intelligence (AI) can be great effective in term of quality systems. Increase in student's strength in class and terrorist attacks on EI urged us to introduce such approach that can assure education quality. Class monitoring with heavy strength always remain major issue for teacher during lecture delivery. In this paper, we implemented reengineering using artificial intelligence based two theories of 1) Multi-face recognition (MFR) system 2) Facial expression recognition (FER) system. Both of these theories supported by intelligent techniques as principal component analysis (PCA), discrete wavelet transform (DWT) and k-nearest neighbor (KNN). After implementation of these intelligent techniques student's attentiveness will increase. Our developed system can detect expressions like happiness, repulsion, fear, anger, and confusion. Student's attentiveness score will be displayed on screen. Teacher can interpret on the basis of attentiveness %age. System decision making can be helpful for class continuity or short break. This system is also an application of an expert system (ES) and knowledge base system (KBS) for educational quality assurance. A similar monitoring system was imposed in china with Hikvision Digital Technology. Predations results proved monitoring can be best way for education quality.

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Section: B Processing Before Recognition (Pre-processing)mentioning

confidence: 99%

Education System re-engineering with AI (artificial intelligence) for Quality Im-provements with proposed model

Muzammul

2019

ADCAIJ

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“…Discrete wavelet transform (DWT) is a frequently used technique because of its satisfactory feature extraction engendered by its spacefrequency localization and multiresolution characteristics. Huang et al [6] proposed a face recognition method by employing a 2D-DWT and new patch strategy. They showed that the method outperformed the traditional 2D-DWT method and a state-of-the-art patch-based method.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Lin

2016

Mathematical Problems in Engineering

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Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.

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“…In early 1990, researchers in face recognition field started using holistic approaches, i.e., facial detection systems use the entire face region as input to accomplish face recognition. In this approach, we find two sub-categories of techniques: the first one is based on linear methods like Eigenfaces principal component analysis (PCA) [11], [12], Fisherfaces linear discriminative analysis (LDA) [13], [14], independent component Int J Elec & Comp Eng ISSN: 2088-8708  analysis (ICA) [15], discrete wavelet transform (DWT) [16] and discrete cosine transform (DCT) [17]. The second technique is based on non-linear methods such as Kernel PCA (KPCA) [18], kernel linear discriminant analysis (KLDA) [19], Gabor-KLDA [20], and CNN [21].…”

Section: Introductionmentioning

confidence: 99%

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Madmoune

Ouariachi

Zenkouar

et al. 2023

IJECE

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<span lang="EN-US">Face recognition is a challenging task due to the complexity of pose variations, occlusion and the variety of face expressions performed by distinct subjects. Thus, many features have been proposed, however each feature has its own drawbacks. Therefore, in this paper, we propose a robust model called Krawtchouk moments convolutional neural networks (KMCNN) for face recognition. Our model is divided into two main steps. Firstly, we use 2D discrete orthogonal Krawtchouk moments to represent features. Then, we fed it into convolutional neural networks (CNN) for classification. The main goal of the proposed approach is to improve the classification accuracy of noisy grayscale face images. In fact, Krawtchouk moments are less sensitive to noisy effects. Moreover, they can extract pertinent features from an image using only low orders. To investigate the robustness of the proposed approach, two types of noise (salt and pepper and speckle) are added to three datasets (YaleB extended, our database of faces (ORL), and a subset of <a name="_Hlk128818620"></a>labeled faces in the wild (LFW)). Experimental results show that KMCNN is flexible and performs significantly better than using just CNN or when we combine it with other discrete moments such as Tchebichef, Hahn, Racah moments in most densities of noises.</span>

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Non-uniform patch based face recognition via 2D-DWT

Cited by 42 publications

References 28 publications

Education System re-engineering with AI (artificial intelligence) for Quality Im-provements with proposed model

Education System re-engineering with AI (artificial intelligence) for Quality Im-provements with proposed model

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

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