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Proceedings ArticleDOI

Face detection system using FPGA

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TLDR
This paper is reading various facial images and storing them, obtaining special features of the face such as Lip portion or Eye portion and subtract test images with stored images and compare the subtracted value with threshold limit for detection.
Abstract
In recent times, face recognition is developing rapidly for its various applications in the field of Human-Interface, Biometrics, Robotics, Security, Surveillance and other commercial use. Biometric based advancements and technologies include identification associated with the biological characteristics (finger print, retina scanning) and behavioral traits (mood detection). Out of the available recognition sensors, facial image recognition is highly recommended because of its easy to use(cameras), reasonable cost, precise measurement and other features. In this paper, we are reading various facial images and storing them. Image test benches are read in our Verilog program and stored in memories. We compare images bit by bit and check if there is any mismatch. If image is matched then we display "Match found" otherwise "No match found ". In further study, we are obtaining special features of the face such as Lip portion or Eye portion. We subtract test images with stored images and compare the subtracted value with threshold limit for detection.

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Citations
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Proceedings ArticleDOI

Design and Development of Efficient Face Recognition Architecture Using Neural Network on FPGA

TL;DR: The proposed architecture uses Levenberg-Marquardt feed-forward training method for neural network and Histogram of Oriented Gradients features of images as classifier to identify the face among the database of images.
Proceedings ArticleDOI

Hardware Implementation of Pixel Comparison and Error Detection in Image

TL;DR: The image comparison model is used to detect the errors that are concealed in the image and this model is executed on hardware using VerilogHDL.
Book ChapterDOI

Face Recognition for Criminal Identification

TL;DR: In this paper, the authors proposed an alert system to identify suspects on the scene using mobile devices, which can help improve the current identification time by using facial recognition, which was validated in a public area of the Rimac district in Lima, Peru.
References
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Journal ArticleDOI

Face detection in color images

TL;DR: A face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds is proposedBased on a novel lighting compensation technique and a nonlinear color transformation, this method detects skin regions over the entire image and generates face candidates based on the spatial arrangement of these skin patches.
Proceedings ArticleDOI

A real-time face tracker

TL;DR: The authors present a real-time face tracker that can track a person's face while the person moves freely in a room and can be applied to teleconferencing and many HCI applications including lip reading and gaze tracking.

An Image Transform Approach for HMM Based Automatic Lip Reading

G. Potamianos
TL;DR: It is shown that lipreading performance dramatically deteriorates below a 10 Hz field rate, and that image transform features are robust to noise and compression artifacts.
Proceedings ArticleDOI

An image transform approach for HMM based automatic lipreading

TL;DR: In this paper, two approaches for extracting features relevant to lipreading, given image sequences of the speaker's mouth region, are considered: a lip contour based feature approach which first obtains estimates of speaker's lip contours and subsequently extracts features from them; and an image transform based approach, which obtains a compressed representation of the image pixel values that contain the speaker mouth.
Proceedings ArticleDOI

Face locating and tracking for human-computer interaction

TL;DR: A connectionist face tracker that manipulates camera orientation and room, to keep a person's face located at all times is proposed, which operates in real time and can adapt rapidly to different lighting conditions, cameras and faces, making it robust against environmental variability.
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