Robust Face Recognition Approaches Using PCA, ICA, LDA Based on DWT, and SVM Algorithms

Lahaw, Zied Bannour; Essaidani, Dhekra; Seddik, Hassene

doi:10.1109/tsp.2018.8441452

Cited by 39 publications

(13 citation statements)

References 10 publications

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“…Typically, object recognition algorithms operate in two stages [ 3 , 54 ]. The first stage extracts features from the image using methods such as LBP [ 55 , 56 , 57 ], deformable part-based models (DPM) [ 58 , 59 ], or a histogram of oriented gradients (HOG) [ 60 , 61 , 62 ]. The second stage uses the feature vector to label the image, using classification methods such as nearest neighbors [ 63 , 64 ], support vector machines (SVM) [ 64 , 65 ], or deep neural networks [ 66 , 67 ].…”

Section: Methodsmentioning

confidence: 99%

Face Recognition on a Smart Image Sensor Using Local Gradients

Valenzuela

Soto

Zarkesh-Ha

et al. 2021

Sensors

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In this paper, we present the architecture of a smart imaging sensor (SIS) for face recognition, based on a custom-design smart pixel capable of computing local spatial gradients in the analog domain, and a digital coprocessor that performs image classification. The SIS uses spatial gradients to compute a lightweight version of local binary patterns (LBP), which we term ringed LBP (RLBP). Our face recognition method, which is based on Ahonen’s algorithm, operates in three stages: (1) it extracts local image features using RLBP, (2) it computes a feature vector using RLBP histograms, (3) it projects the vector onto a subspace that maximizes class separation and classifies the image using a nearest neighbor criterion. We designed the smart pixel using the TSMC 0.35 μm mixed-signal CMOS process, and evaluated its performance using postlayout parasitic extraction. We also designed and implemented the digital coprocessor on a Xilinx XC7Z020 field-programmable gate array. The smart pixel achieves a fill factor of 34% on the 0.35 μm process and 76% on a 0.18 μm process with 32 μm × 32 μm pixels. The pixel array operates at up to 556 frames per second. The digital coprocessor achieves 96.5% classification accuracy on a database of infrared face images, can classify a 150×80-pixel image in 94 μs, and consumes 71 mW of power.

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Section: Methodsmentioning

confidence: 99%

Face Recognition on a Smart Image Sensor Using Local Gradients

Valenzuela

Soto

Zarkesh-Ha

et al. 2021

Sensors

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“…Still determination of classification threshold parameter is a major issue that affects the recognition accuracy of the model. Lahaw et al [44] proposed SVM model to classify face images based on ICA, PCA and LDA features. However, it is inadequate for pose variant expressions.…”

Section: Related Workmentioning

confidence: 99%

“…It is evident from these observations that proposed algorithm is effective in classifying face images captured at varying illumination conditions, different facial expressions, and behavior. Table 6 shows that for ORL database, 4-state HMM has better recognition rate than other techniques proposed in [7], [18], [35], [39], [40], [42], [43], [44] and [45] except [26] in which 100% recognition rate was claimed. However, computational complexity of [26] is much higher than our proposed HMM model for FR.…”

Section: Comparative Studymentioning

confidence: 99%

Face Recognition System Based on Four State Hidden Markov Model

et al. 2022

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Computational complexity is a matter of great concern in real time face recognition systems. In this paper, four state hidden Markov model for face recognition has been presented whereby coefficients of feature vectors have been curtailed. Face images have been divided into a sequence of overlapping blocks. An observation sequence containing coefficients of eigen values and eigenvectors of these blocks have been used to train the model and each subject is associated with a separate hidden Markov model. The computational complexity of the proposed model has been minimized by employing discrete wavelet transform in the preprocessing stage. Furthermore, singular value decomposition has been employed on face images and a threshold singular value is determined empirically to reject or accept test images. Principal component analysis is used for feature extraction. Accepted test images are classified based on the majority vote criteria using different observation sequences of image features. Experimental findings on Yale and ORL databases in noisy such as Salt and Pepper and noise free environments reveal that the recognition accuracy of the proposed model is comparable to the existing techniques with reduced computational cost.

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“…They also evaluated LDA, Multilayer perceptron, Naïve Bayes and SVM and obtained the accuracy of 97% and while using PCA and LDA the accuracy was 100%. Lahaw et al, [52] used LDA, IDA,PCA and SVM for face recognition. They experiment the FD system with these ML algorithms using AT&T database.…”

Section: Review About the Face Detection Approaches Using Ml/ Deep Learning Algorithmsmentioning

confidence: 99%

Study Of Deep Learning Techniques For Differently Abled Applications And Detection Techniques

Anandh¹

2021

TURCOMAT

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In worldwide, Visually Impaired Persons (VIP) are facing several issue related to visual impairment and blindness and they are assisted with technical inventions. Based on the survey from World Health Organization (WHO), around 2.2 billion peoples are suffered from visual impairment and among them 1 million peoples are suffered by blindness. Vision is the major sensing organ of the human and to assist the VIP regarding this, there are various digital vision products are in the market which is based on digital technologies and advanced algorithms. This will transform the VIP’s vision world into audio to get to know about their surroundings includes objects, motion, obstacles and spatial locations. The objective of this paper is to provide the detailed survey about the existing object recognition, face recognition and text to voice recognition methods proposed to assist VIP. Due to the increase development of machine learning and deep learning algorithms, the digital image recognition and object recognition are become more efficient. These advanced technologies can make sure to assist the VIP to detect and recognize the people face and objects in front of them in the form of audio that are practiced by them on daily basis.

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Robust Face Recognition Approaches Using PCA, ICA, LDA Based on DWT, and SVM Algorithms

Cited by 39 publications

References 10 publications

Face Recognition on a Smart Image Sensor Using Local Gradients

Face Recognition on a Smart Image Sensor Using Local Gradients

Face Recognition System Based on Four State Hidden Markov Model

Study Of Deep Learning Techniques For Differently Abled Applications And Detection Techniques

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