66 research outputs found
Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour
Automatic facial expression recognition is widely used for various applications such as health care, surveillance and human-robot interaction. In this paper, we present a novel system which employs automatic facial emotion recognition technique for adaptive AI agent behaviour. The proposed system is equipped with kirsch operator based local binary patterns for feature extraction and diverse classifiers for emotion recognition. First, we nominate a novel variant of the local binary pattern (LBP) for feature extraction to deal with illumination changes, scaling and rotation variations. The features extracted are then used as input to the classifier for recognizing seven emotions. The detected emotion is then used to enhance the behaviour selection of the artificial intelligence (AI) agents in a shooter game. The proposed system is evaluated with multiple facial expression datasets and outperformed other state-of-the-art models by a significant margin
Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review
This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen
Hierarchical age estimation using enhanced facial features.
Doctor of Philosopy in Computer Science, University of KwaZulu-Natal, Westville, 2018.Ageing is a stochastic, inevitable and uncontrollable process that constantly affect
shape, texture and general appearance of the human face. Humans can easily determine
ones’ gender, identity and ethnicity with highest accuracy as compared to
age. This makes development of automatic age estimation techniques that surpass
human performance an attractive yet challenging task. Automatic age estimation
requires extraction of robust and reliable age discriminative features. Local binary
patterns (LBP) sensitivity to noise makes it insufficiently reliable in capturing age
discriminative features. Although local ternary patterns (LTP) is insensitive to noise,
it uses a single static threshold for all images regardless of varied image conditions.
Local directional patterns (LDP) uses k directional responses to encode image gradient
and disregards not only central pixel in the local neighborhood but also 8 k
directional responses. Every pixel in an image carry subtle information. Discarding
8 k directional responses lead to lose of discriminative texture features. This
study proposes two variations of LDP operator for texture extraction. Significantorientation
response LDP (SOR-LDP) encodes image gradient by grouping eight
directional responses into four pairs. Each pair represents orientation of an edge
with respect to central reference pixel. Values in each pair are compared and the
bit corresponding to the maximum value in the pair is set to 1 while the other is
set to 0. The resultant binary code is converted to decimal and assigned to the central
pixel as its’ SOR-LDP code. Texture features are contained in the histogram of
SOR-LDP encoded image. Local ternary directional patterns (LTDP) first gets the
difference between neighboring pixels and central pixel in 3 3 image region. These
differential values are convolved with Kirsch edge detectors to obtain directional
responses. These responses are normalized and used as probability of an edge occurring
towards a respective direction. An adaptive threshold is applied to derive
LTDP code. The LTDP code is split into its positive and negative LTDP codes. Histograms
of negative and positive LTDP encoded images are concatenated to obtain
texture feature. Regardless of there being evidence of spatial frequency processing
in primary visual cortex, biologically inspired features (BIF) that model visual cortex
uses only scale and orientation selectivity in feature extraction. Furthermore,
these BIF are extracted using holistic (global) pooling across scale and orientations
leading to lose of substantive information. This study proposes multi-frequency BIF
(MF-BIF) where frequency selectivity is introduced in BIF modelling. Local statistical
BIF (LS-BIF) uses local pooling within scale, orientation and frequency in n n
region for BIF extraction. Using Leave-one-person-out (LOPO) validation protocol,
this study investigated performance of proposed feature extractors in age estimation
in a hierarchical way by performing age-group classification using Multi-layer
Perceptron (MLP) followed by within age-group exact age regression using support
vector regression (SVR). Mean absolute error (MAE) and cumulative score (CS) were
used to evaluate performance of proposed face descriptors. Experimental results on
FG-NET ageing dataset show that SOR-LDP, LTDP, MF-BIF and LS-BIF outperform
state-of-the-art feature descriptors in age estimation. Experimental results show that
performing gender discrimination before age-group and age estimation further improves
age estimation accuracies. Shape, appearance, wrinkle and texture features
are simultaneously extracted by visual system in primates for the brain to process
and understand an image or a scene. However, age estimation systems in the literature
use a single feature for age estimation. A single feature is not sufficient enough
to capture subtle age discriminative traits due to stochastic and personalized nature
of ageing. This study propose fusion of different facial features to enhance their
discriminative power. Experimental results show that fusing shape, texture, wrinkle
and appearance result into robust age discriminative features that achieve lower
MAE compared to single feature performance
QUAD FLAT NO-LEAD (QFN) DEVICE FAULTY DETECTION USING GABOR WAVELETS
Computer vision inspection system using image processing algorithms have
been utilized by many manufacturing companies as a method of quality control. Since
manufacturing industries comprise of many types of products, various image processing
algorithms have been developed to suit different type of outputting products. In this
paper, we explored Gabor wavelet feature extraction as a method for vision inspection.
Unlike conventional vision inspection system which require manual human
configuration of inspection algorithms, our experiment uses Gabor wavelets to
fractionate the image into distinctive scales and orientations. Through chi-square
distance computation, the physical quality of Quad Flan No-Lead (QFN) device can be
distinguished by computing the dissimilarity of the test image with the trained database,
thus eliminating the weakness of human errors in configuration of vision systems. We
performed our algorithm testing using 64 real-world production images obtained from a
0.3 megapixel monochromatic industrial smart vision camera. The images consists a
mixture of physically good and defected QFN units. The proposed algorithm achieved
98.46% accuracy rate with the average processing time of 0.457 seconds per image
Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour
This publication may contain explicit sexual literary descriptions and/or artistic depictions
An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements
Spectral imaging has recently gained traction for face recognition in
biometric systems. We investigate the merits of spectral imaging for face
recognition and the current challenges that hamper the widespread deployment of
spectral sensors for face recognition. The reliability of conventional face
recognition systems operating in the visible range is compromised by
illumination changes, pose variations and spoof attacks. Recent works have
reaped the benefits of spectral imaging to counter these limitations in
surveillance activities (defence, airport security checks, etc.). However, the
implementation of this technology for biometrics, is still in its infancy due
to multiple reasons. We present an overview of the existing work in the domain
of spectral imaging for face recognition, different types of modalities and
their assessment, availability of public databases for sake of reproducible
research as well as evaluation of algorithms, and recent advancements in the
field, such as, the use of deep learning-based methods for recognizing faces
from spectral images
Multi-Modality Human Action Recognition
Human action recognition is very useful in many applications in various areas, e.g. video surveillance, HCI (Human computer interaction), video retrieval, gaming and security. Recently, human action recognition becomes an active research topic in computer vision and pattern recognition. A number of action recognition approaches have been proposed. However, most of the approaches are designed on the RGB images sequences, where the action data was collected by RGB/intensity camera. Thus the recognition performance is usually related to various occlusion, background, and lighting conditions of the image sequences. If more information can be provided along with the image sequences, more data sources other than the RGB video can be utilized, human actions could be better represented and recognized by the designed computer vision system.;In this dissertation, the multi-modality human action recognition is studied. On one hand, we introduce the study of multi-spectral action recognition, which involves the information from different spectrum beyond visible, e.g. infrared and near infrared. Action recognition in individual spectra is explored and new methods are proposed. Then the cross-spectral action recognition is also investigated and novel approaches are proposed in our work. On the other hand, since the depth imaging technology has made a significant progress recently, where depth information can be captured simultaneously with the RGB videos. The depth-based human action recognition is also investigated. I first propose a method combining different type of depth data to recognize human actions. Then a thorough evaluation is conducted on spatiotemporal interest point (STIP) based features for depth-based action recognition. Finally, I advocate the study of fusing different features for depth-based action analysis. Moreover, human depression recognition is studied by combining facial appearance model as well as facial dynamic model
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