Optimized face detection on FPGA

Chakrasali, Sadashiva V.; Kuthale, Sanmati

doi:10.1109/cimca.2016.8053269

Cited by 5 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This machine learning technique is the newest that has been implemented with FPGAs in our list (starting in 2005). It has been widely applied for image processing in face detection [524][525][526][527][528] and also for human detection [501,529].…”

Section: Machine Learningmentioning

confidence: 99%

Field Programmable Gate Array Applications—A Scientometric Review

2019

View full text Add to dashboard Cite

Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

show abstract

Section: Machine Learningmentioning

confidence: 99%

Field Programmable Gate Array Applications—A Scientometric Review

2019

View full text Add to dashboard Cite

show abstract

“…They have used an image size of 320 × 240 pixels and got comparatively inferior performance with respect to the software-based system executed on CPUs. Chakrasali and Kuthale [8] used Haar features and the AdaBoost algorithm for face detection.…”

Section: Introductionmentioning

confidence: 99%

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

Paul¹,

Aslan²

2021

OPJ

View full text Add to dashboard Cite

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30˚ on either side from the front face showed an average face recognition precision of 72.25% -81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

show abstract

MULTI-modal Imaging of FOREnsic SciEnce Evidence: MULTI-FORESEE Project

Sklavos

Francese

2020

2020 23rd Euromicro Conference on Digital System Design (DSD)

View full text Add to dashboard Cite

Optimized face detection on FPGA

Cited by 5 publications

References 3 publications

Field Programmable Gate Array Applications—A Scientometric Review

Field Programmable Gate Array Applications—A Scientometric Review

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

MULTI-modal Imaging of FOREnsic SciEnce Evidence: MULTI-FORESEE Project

Contact Info

Product

Resources

About