Hardware implementation for face detection on Xilinx Virtex-II FPGA using the reversible component transformation colour space

2006 ◽  
Author(s):  
M. Po-Leen Ooi
Keyword(s):  
Face Detection ◽  
Hardware Implementation ◽  
Colour Space ◽  
Reversible Component

Chromatic Cues for Face Detection in Natural Scenes

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 294-294
Author(s):  
A Oliva ◽  
S Akamatsu ◽  
P G Schyns
Keyword(s):  
Face Detection ◽  
Reaction Times ◽  
Detection Task ◽  
Natural Scenes ◽  
Colour Space ◽  
Affect Detection ◽  
Detection Of Objects ◽  
Chromatic Cues ◽  
Colour Histograms ◽  
Euclidian Distances

One of the challenging problems of human and machine vision is the detection of objects against complex backgrounds. Our research addresses the question of how faces can be very quickly detected in naturalistic scenes on the basis of luminance and chromatic cues. Although luminance information varies with pose and illumination differences, chromatic information is by and large invariant under these transformations. Hence, chromatic information might be a very powerful cue for segmentation and detection. We compared faces of different pigmentation against background scenes of different colours. Specifically, colour histograms were computed in a perceptually uniform colour space (L*u*v*). We computed the Euclidian distances between the averages of the colour histograms of faces and scenes in L*u*v*. This metric was used to calibrate the contrast between face and scene colour in the experimental design. In a face detection task, subjects saw faces against scene backgrounds at a different distance in colour space. Each combination face - scene was presented for 120 ms (to prevent saccadic explorations), and the subject's task was to indicate whether or not a face was present. Controls involved face - scene pairs on an isoluminant background. Results revealed that luminance information did not affect detection on the basis of chromatic cues. Importantly, the metric of detectability in L*u*v* space between scene and faces predicted reaction times to detection.

Hardware Implementation Of Iris Matching

2012 ◽  
Vol 1 (6) ◽  
pp. 46-48
Author(s):  
Bethuna Bethuna ◽  
Keyword(s):  
Hardware Implementation

Face Detection for AIBO Using RBF Network

2010 ◽  
Vol 130 (11) ◽  
pp. 2031-2038
Author(s):  
Kohki Abiko ◽  
Hironobu Fukai ◽  
Yasue Mitsukura ◽  
Minoru Fukumi ◽  
Masahiro Tanaka
Keyword(s):  
Face Detection ◽  
Rbf Network

An Improvement of Rate-Distortion Optimized Quantization Process and its Hardware Implementation

2015 ◽  
Vol 135 (11) ◽  
pp. 1299-1306
Author(s):  
Genki Moriguchi ◽  
Takashi Kambe ◽  
Gen Fujita ◽  
Hajime Sawano
Keyword(s):  
Hardware Implementation ◽  
Rate Distortion

Colour Image Enhancement using Perceptual Saturation and Vividness

2019 ◽  
Vol 2019 (1) ◽  
pp. 243-246
Author(s):  
Muhammad Safdar ◽  
Noémie Pozzera ◽  
Jon Yngve Hardeberg
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Test Performance ◽  
Appearance Model ◽  
Colour Space ◽  
Categorical Judgment ◽  
Saturation Scale ◽  
Colour Image ◽  
Colour Appearance ◽  
Future Test

A perceptual study was conducted to enhance colour image quality in terms of naturalness and preference using perceptual scales of saturation and vividness. Saturation scale has been extensively used for this purpose while vividness has been little used. We used perceptual scales of a recently developed colour appearance model based on Jzazbz uniform colour space. A two-fold aim of the study was (i) to test performance of recently developed perceptual scales of saturation and vividness compared with previously used hypothetical models and (ii) to compare performance and chose one of saturation and vividness scales for colour image enhancement in future. Test images were first transformed to Jzazbz colour space and their saturation and vividness were then decreased or increased to obtain 6 different variants of the image. Categorical judgment method was used to judge preference and naturalness of different variants of the test images and results are reported.

Opponent Center-Surround Contrast: Colour to Grey Conversion

2020 ◽  
Vol 2020 (1) ◽  
pp. 105-108
Author(s):  
Ali Alsam
Keyword(s):  
Evolutionary Algorithms ◽  
Three Dimensional ◽  
The State ◽  
Colour Space ◽  
Vision Science ◽  
State Of Art ◽  
Over Time

Vision is the science that informs us about the biological and evolutionary algorithms that our eyes, opticnerves and brains have chosen over time to see. This article is an attempt to solve the problem of colour to grey conversion, by borrowing ideas from vision science. We introduce an algorithm that measures contrast along the opponent colour directions and use the results to combine a three dimensional colour space into a grey. The results indicate that the proposed algorithm competes with the state of art algorithms.

Speaker Identity Recognition by Acoustic and Visual Data Fusion through Personal Privacy for Smart Care and Service Applications

2020 ◽  
Vol 64 (4) ◽  
pp. 40404-1-40404-16
Author(s):  
I.-J. Ding ◽  
C.-M. Ruan
Keyword(s):  
Face Detection ◽  
Speaker Recognition ◽  
Visual Information ◽  
Classification Tree ◽  
Gaussian Mixture ◽  
Recognition Method ◽  
Indoor Space ◽  
Identity Recognition ◽  
Visual Identity ◽  
Speaker Classification

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

Efficient Face Detection on Epiphany Multicore Processor

2017 ◽  
Vol 1 (1) ◽  
pp. 113-127 ◽  
Author(s):  
A. A. Sukhinov ◽  
◽  
G. B. Ostrobrod ◽  
Keyword(s):  
Face Detection ◽  
Multicore Processor
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