An Overview of Biometrics Methods

Sharif, Muhammad; Raza, Mudassar; Shah, Jamal Hussain; Yasmin, Mussarat; Fernandes, Steven Lawrence

doi:10.1007/978-3-030-15887-3_2

Cited by 39 publications

(19 citation statements)

References 58 publications

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“…The CTIA is configured in direct mode: sw1 and sw4 are closed, and sw2 and sw3 are open. The equivalent circuit of Figure 7b shows the CTIA acting as a conventional integrator, and Equation (2) shows how it computes the output voltage that represents the local pixel value:…”

Section: Smart Pixelmentioning

confidence: 99%

“…The attention of the scientific and industrial community in image-based biometric methods has fostered a growing interest in smart imaging systems (SIS) that can handle the computational requirements of real-time video analysis. Biometrics is described as a pattern-recognition technique for individual identification, based on their physical, chemical, or behavioral characteristics [ 1 , 2 ]. One of the most popular biometric techniques is face recognition [ 2 ], which has abundant applications [ 3 , 4 , 5 ] in various areas, such as: (1) security, including identity verification [ 6 , 7 ], computer or mobile device unlock [ 7 , 8 ], criminal records search, and voter registration; (2) surveillance, such as cameras used on closed circuit television (CCTV) [ 9 ]; and (3) access control that could grant access to a specific place or an electronic account to a group of people [ 10 ] using their faces as a credential.…”

Section: Introductionmentioning

confidence: 99%

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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: Smart Pixelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face Recognition on a Smart Image Sensor Using Local Gradients

Valenzuela

Soto

Zarkesh-Ha

et al. 2021

Sensors

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“…These grain crops and legumes having rich source of nutrients, minerals, dietary fibers that directly related to the humans heath and prevent from health disorders. But due to pandemic situation like COVID-19 it cause the instability in the economy; in such situation food security becomes the important concern of the modern world to secure the future supply, as according to United Nations (UN) report the population of the world increase by 8 billion people in 2023 and 10 billion in 2050 [3]. Due to some factors like non-identification and late detection of diseases can waste up to 40% of agriculture products [4].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Various Unimodal Biometric Techniques

Mehmood¹,

Mishra²

2021

STAIQC

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The agriculture plays very important role as it helps to accomplish the need of food among people. The production in agriculture consequentially contributes to the economy of every country. The grain crops rice, wheat, maize, and legumes are suffering a lot due to some viral, bacterial, and fungal diseases. The pest and variety of diseases can bring a heavy loss to the global economy. The monitoring of crops health and identification of diseases at early days is very challenging and emerging task in agriculture. So, it is very important to prevent crops from fatal diseases in the early stage, but the manual process of disease discovery can lead to erroneous magnitude of pesticides. The trouble is figure out by automate discovery of diseases and supplication of relevant medication on time. It is very necessary to find out accurate disease to overcome heavy loss to economy. From the few decades, to detect disease correctly, the process of detection become automate using emerging technologies and techniques using computer vision, machine learning and image processing. This article presents the extensive literature on existing methodologies utilized for recognition and classification of leaves diseases. The studies addresses that there is still many limitations and challenges find in different phases in plant disease detection system. The presented research also highlights the pros and cons of different techniques that help out the researchers for contribution in future.

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“…Through the surveillance, there are huge video data gathered through these systems, and to keep al., 2018), and person re-identification (Fayyaz, M., Yasmin, M., et al, 2020;Rehman, S. U., Chen, Z., et al, 2018;Rehman, S., Chen, Z., et al, 2016, July;Hussain, S. J., Chen, Z., et al, 2016, January, etc. ), biometrics (Sharif, M., Raza, M., et al, 2019;Azeem, A., Sharif, M., et al, 2015), and Satellite imaging (Sharif, M., Khan, S., et al. 2019, April) etc.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Gender Recognition With Handcrafted Feature Ensembles

Ahad¹,

Fayyaz²

2021

AzJHPC

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Human gender recognition is one the most challenging task in computer vision, especially in pedestrians, due to so much variation in human poses, video acquisition, illumination, occlusion, and human clothes, etc. In this article, we have considered gender recognition which is very important to be considered in video surveillance. To make the system automated to recognize the gender, we have provided a novel technique based on the extraction of features through different methodologies. Our technique consists of 4 steps a) preprocessing, b) feature extraction, c) feature fusion, d) classification. The exciting area is separated in the first step, which is the full body from the images. After that, images are divided into two halves on the ratio of 2:3 to acquire sets of upper body and lower body. In the second step, three handcrafted feature extractors, HOG, Gabor, and granulometry, extract the feature vectors using different score values. These feature vectors are fused to create one strong feature vector on which results are evaluated. Experiments are performed on full-body datasets to make the best configuration of features. The features are extracted through different feature extractors in different numbers to generate their feature vectors. Those features are fused to create a strong feature vector. This feature vector is then utilized for classification. For classification, SVM and KNN classifiers are used. Results are evaluated on five performance measures: Accuracy, Precision, Sensitivity, Specificity, and Area under the curve. The best results that have been acquired are on the upper body, which is 88.7% accuracy and 0.96 AUC. The results are compared with the existing methodologies, and hence it is concluded that the proposed method has significantly achieved higher results.

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An Overview of Biometrics Methods

Cited by 39 publications

References 58 publications

Face Recognition on a Smart Image Sensor Using Local Gradients

Face Recognition on a Smart Image Sensor Using Local Gradients

A Survey on Various Unimodal Biometric Techniques

Pedestrian Gender Recognition With Handcrafted Feature Ensembles

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