Software Development Framework for Real-Time Face Detection and Recognition in Mobile Devices

Rai, Laxmisha; Wang, Zhiyuan; Rodrigo, Amila; Deng, Zhaopeng; Liu, Haiqing

doi:10.3991/ijim.v14i04.12077

Cited by 9 publications

(9 citation statements)

References 20 publications

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“…To determine the pattern range, the lowest and highest start positions of size 0.48923634 frequencies in the 5 data sets are taken. From Table8, the pattern-range is (10)(11)(12)(13)(14). = 1 passes the 0.7 threshold.…”

Section: Methodsmentioning

confidence: 99%

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Password-free Authentication for Smartphone Touchscreen Based on Finger Size Pattern

Enaya

Mohammed

Bilal³

2020

Int. J. Interact. Mob. Technol.

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This study introduces a novel authentication methodology; it is based on pattern recognition of fingers size and pressure when users touch smartphone screen. By analyzing diagrams of these touches and applying data mining for the first time as an authentication technique, this paper presents three new approaches. First, an exact-range evaluation approach has been verified that size is more recognition consistency than pressure. Second, a pattern-range is a new technique reliance on size frequency position. At last, using a size-range has been facilitated the login. The association rules have been modified to work on finger touchscreen data files. To login, 94.1111% of 18 authorized users are succeeded and 98.9% of 20 unauthorized users are failed. Android device and Android studio are used. Size and pressure are normalized to 1; a training set is applied; the password is not considered.

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Section: Methodsmentioning

confidence: 99%

“…[11] is another study where they handled distinguishing between soft and hard finger pressure, based on users' features such as small fingers and speed. [12] proposes another powerful Android software development framework that uses OpenCV library for real-time face recognition.…”

Section: Related Workmentioning

confidence: 99%

Password-free Authentication for Smartphone Touchscreen Based on Finger Size Pattern

Enaya

Mohammed

Bilal³

2020

Int. J. Interact. Mob. Technol.

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“…Rai et al [ 21 ] proposed a face detection system for real-time operation in mobile devices. The system is based on OpenCV, a library for real-time computer vision applications, and has layers for image preprocessing and face detection.…”

Section: Related Workmentioning

confidence: 99%

A face recognition software framework based on principal component analysis

et al. 2021

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Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

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“…Kakarla develops new CNN architecture for face recognition in the attendance system, achieving 99% accuracy [3]. Rai also successfully created real-time face detection and recognition using a smartphone camera [4]. However, both research still deals with the ideal condition of the front face image.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Camera-Based Smart Surveillance System for Detecting, Recognizing, and Tracking Masked Human Face

Haryadi

Jati

Husodo

et al. 2021

Int. J. Interact. Mob. Technol.

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A surveillance system is still the most exciting and practical security system to prevent crime effectively. The primary purpose of this system is to recognize the identity of the face caught by the camera. With the advancement of the Internet of things, surveillance systems were implemented on edge devices such as the low-cost Raspberry mobile camera. It raises the challenge of unstructured image/video where the video contains low quality, blur, and variations of human poses. The challenge is increasing because people used to wear a mask during the Covid -19 pandemic. Therefore, we proposed developing an all-in-one surveillance system with face detection, recognition, and face tracking capabilities. This system integrated three modules: MTCNN face detector, VGGFace2 face recognition, and Discriminative Single-Shot Segmentation (D3S) tracker to create a system capable of tracking the faces of people caught on surveillance camera. We also train new face mask data to recognize and track. This system obtains data from the Raspberry Pi camera and processes images on the cloud as a mobile sensor approach. The proposed system successfully implemented and obtained competitive results in detection, recognition, and tracking under an unconstrained surveillance camera.

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Software Development Framework for Real-Time Face Detection and Recognition in Mobile Devices

Cited by 9 publications

References 20 publications

Password-free Authentication for Smartphone Touchscreen Based on Finger Size Pattern

Password-free Authentication for Smartphone Touchscreen Based on Finger Size Pattern

A face recognition software framework based on principal component analysis

Low-Cost Camera-Based Smart Surveillance System for Detecting, Recognizing, and Tracking Masked Human Face

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