514 research outputs found
Une méthode de reconnaissance des expressions du visage basée sur la perception
Session "Atelier VISAGES"National audienceLes humains peuvent reconnaître très facilement les expressions du visage en temps réel. Toutefois, la reconnaissance fiable et rapide des expressions faciales en temps réel est une tâche difficile pour un ordinateur. Nous présentons une nouvelle approche de reconnaissance de trois type d'expressions faciales qui se base sur l'idée de ne considérer que de petites régions du visage bien définies pour en extraire les caractéristiques. Cette proposition est basée sur une étude psycho-visuel expérimental menée avec un eye-tracker. Les mouvements des yeux de quinze sujets ont été enregistrés dans des conditions de visualisation libre d'une collection de 54 vidéos montrant six expressions faciales universelles. Les résultats de cette étude montrent que pour certaines expressions du visage une unique région est perceptuellement plus attractive que les autres. Les autres expressions montrent une attractivité pour deux ou trois régions du visage. Cette connaissance est utilisée pour définir une méthode de reconnaissance se concentrant uniquement sur certaines régions perceptuellement attrayantes du visage et ainsi réduire par un facteur de deux les temps de calcul. Nos résultats montrent une précision de reconnaissance automatique de trois expressions de 99.5% sur la base de données d'expression faciale Cohn-Kanade
QUIS-CAMPI: Biometric Recognition in Surveillance Scenarios
The concerns about individuals security have justified the increasing number of surveillance
cameras deployed both in private and public spaces. However, contrary to popular belief,
these devices are in most cases used solely for recording, instead of feeding intelligent analysis
processes capable of extracting information about the observed individuals. Thus, even though
video surveillance has already proved to be essential for solving multiple crimes, obtaining relevant
details about the subjects that took part in a crime depends on the manual inspection
of recordings. As such, the current goal of the research community is the development of
automated surveillance systems capable of monitoring and identifying subjects in surveillance
scenarios. Accordingly, the main goal of this thesis is to improve the performance of biometric
recognition algorithms in data acquired from surveillance scenarios. In particular, we aim at
designing a visual surveillance system capable of acquiring biometric data at a distance (e.g.,
face, iris or gait) without requiring human intervention in the process, as well as devising biometric
recognition methods robust to the degradation factors resulting from the unconstrained
acquisition process.
Regarding the first goal, the analysis of the data acquired by typical surveillance systems
shows that large acquisition distances significantly decrease the resolution of biometric samples,
and thus their discriminability is not sufficient for recognition purposes. In the literature,
diverse works point out Pan Tilt Zoom (PTZ) cameras as the most practical way for acquiring
high-resolution imagery at a distance, particularly when using a master-slave configuration. In
the master-slave configuration, the video acquired by a typical surveillance camera is analyzed
for obtaining regions of interest (e.g., car, person) and these regions are subsequently imaged
at high-resolution by the PTZ camera. Several methods have already shown that this configuration
can be used for acquiring biometric data at a distance. Nevertheless, these methods
failed at providing effective solutions to the typical challenges of this strategy, restraining its
use in surveillance scenarios. Accordingly, this thesis proposes two methods to support the development
of a biometric data acquisition system based on the cooperation of a PTZ camera
with a typical surveillance camera. The first proposal is a camera calibration method capable
of accurately mapping the coordinates of the master camera to the pan/tilt angles of the PTZ
camera. The second proposal is a camera scheduling method for determining - in real-time -
the sequence of acquisitions that maximizes the number of different targets obtained, while
minimizing the cumulative transition time. In order to achieve the first goal of this thesis,
both methods were combined with state-of-the-art approaches of the human monitoring field
to develop a fully automated surveillance capable of acquiring biometric data at a distance and
without human cooperation, designated as QUIS-CAMPI system.
The QUIS-CAMPI system is the basis for pursuing the second goal of this thesis. The analysis
of the performance of the state-of-the-art biometric recognition approaches shows that these
approaches attain almost ideal recognition rates in unconstrained data. However, this performance
is incongruous with the recognition rates observed in surveillance scenarios. Taking into
account the drawbacks of current biometric datasets, this thesis introduces a novel dataset comprising
biometric samples (face images and gait videos) acquired by the QUIS-CAMPI system at a
distance ranging from 5 to 40 meters and without human intervention in the acquisition process.
This set allows to objectively assess the performance of state-of-the-art biometric recognition
methods in data that truly encompass the covariates of surveillance scenarios. As such, this set
was exploited for promoting the first international challenge on biometric recognition in the wild. This thesis describes the evaluation protocols adopted, along with the results obtained
by the nine methods specially designed for this competition. In addition, the data acquired by
the QUIS-CAMPI system were crucial for accomplishing the second goal of this thesis, i.e., the
development of methods robust to the covariates of surveillance scenarios. The first proposal
regards a method for detecting corrupted features in biometric signatures inferred by a redundancy
analysis algorithm. The second proposal is a caricature-based face recognition approach
capable of enhancing the recognition performance by automatically generating a caricature
from a 2D photo. The experimental evaluation of these methods shows that both approaches
contribute to improve the recognition performance in unconstrained data.A crescente preocupação com a segurança dos indivĂduos tem justificado o crescimento
do nĂşmero de câmaras de vĂdeo-vigilância instaladas tanto em espaços privados como pĂşblicos.
Contudo, ao contrário do que normalmente se pensa, estes dispositivos são, na maior parte dos
casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente
capaz de inferir em tempo real informações sobre os indivĂduos observados. Assim, apesar de a
vĂdeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda
confinado Ă disponibilização de vĂdeos que tĂŞm que ser manualmente inspecionados para extrair
informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal
desafio da comunidade cientĂfica Ă© o desenvolvimento de sistemas automatizados capazes de
monitorizar e identificar indivĂduos em ambientes de vĂdeo-vigilância.
Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento
biomĂ©trico aos ambientes de vĂdeo-vigilância. De forma mais especifica, pretende-se
1) conceber um sistema de vĂdeo-vigilância que consiga adquirir dados biomĂ©tricos a longas distâncias
(e.g., imagens da cara, Ăris, ou vĂdeos do tipo de passo) sem requerer a cooperação dos
indivĂduos no processo; e 2) desenvolver mĂ©todos de reconhecimento biomĂ©trico robustos aos
fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas.
No que diz respeito ao primeiro objetivo, a análise aos dados adquiridos pelos sistemas tĂpicos
de vĂdeo-vigilância mostra que, devido Ă distância de captura, os traços biomĂ©tricos amostrados
não são suficientemente discriminativos para garantir taxas de reconhecimento aceitáveis.
Na literatura, vários trabalhos advogam o uso de câmaras Pan Tilt Zoom (PTZ) para adquirir
imagens de alta resolução à distância, principalmente o uso destes dispositivos no modo masterslave.
Na configuração master-slave um módulo de análise inteligente seleciona zonas de interesse
(e.g. carros, pessoas) a partir do vĂdeo adquirido por uma câmara de vĂdeo-vigilância
e a câmara PTZ é orientada para adquirir em alta resolução as regiões de interesse. Diversos
métodos já mostraram que esta configuração pode ser usada para adquirir dados biométricos
à distância, ainda assim estes não foram capazes de solucionar alguns problemas relacionados
com esta estratĂ©gia, impedindo assim o seu uso em ambientes de vĂdeo-vigilância. Deste modo,
esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de
vĂdeo-vigilância usando uma câmara PTZ assistida por uma câmara tĂpica de vĂdeo-vigilância. O
primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da câmara
master para o ângulo da câmara PTZ (slave) sem o auxĂlio de outros dispositivos Ăłticos. O
segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela
câmara PTZ. O método proposto consegue determinar em tempo-real a sequência de observações
que maximiza o nĂşmero de diferentes sujeitos observados e simultaneamente minimiza o
tempo total de transição entre sujeitos. De modo a atingir o primeiro objetivo desta tese, os
dois métodos propostos foram combinados com os avanços alcançados na área da monitorização
de humanos para assim desenvolver o primeiro sistema de vĂdeo-vigilância completamente automatizado
e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação
dos indivĂduos no processo, designado por sistema QUIS-CAMPI.
O sistema QUIS-CAMPI representa o ponto de partida para iniciar a investigação relacionada
com o segundo objetivo desta tese. A análise do desempenho dos métodos de reconhecimento
biométrico do estado-da-arte mostra que estes conseguem obter taxas de reconhecimento
quase perfeitas em dados adquiridos sem restrições (e.g., taxas de reconhecimento
maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho nĂŁo Ă© corroborado pelos resultados observados em ambientes de vĂdeo-vigilância, o que sugere que os conjuntos
de dados atuais nĂŁo contĂŞm verdadeiramente os fatores de degradação tĂpicos dos ambientes de
vĂdeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biomĂ©tricos atuais,
esta tese introduz um novo conjunto de dados biomĂ©tricos (imagens da face e vĂdeos do tipo de
passo) adquiridos pelo sistema QUIS-CAMPI a uma distância máxima de 40m e sem a cooperação
dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho
dos mĂ©todos do estado-da-arte no reconhecimento de indivĂduos em imagens/vĂdeos
capturados num ambiente real de vĂdeo-vigilância. Como tal, este conjunto foi utilizado para
promover a primeira competição de reconhecimento biométrico em ambientes não controlados.
Esta tese descreve os protocolos de avaliação usados, assim como os resultados obtidos por 9
métodos especialmente desenhados para esta competição. Para além disso, os dados adquiridos
pelo sistema QUIS-CAMPI foram essenciais para o desenvolvimento de dois métodos para
aumentar a robustez aos fatores de degradação observados em ambientes de vĂdeo-vigilância. O
primeiro Ă© um mĂ©todo para detetar caracterĂsticas corruptas em assinaturas biomĂ©tricas atravĂ©s
da análise da redundância entre subconjuntos de caracterĂsticas. O segundo Ă© um mĂ©todo de
reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma Ăşnica
foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir
as taxas de erro em dados adquiridos de forma nĂŁo controlada
Deteksi Ekspresi Wajah Pada Sekuen Citra Menggunakan Algoritma Active Shape Model Dan Klasifier Twofold Random Forest
Ekspresi wajah adalah informasi nonverbal yang penting
untuk melengkapi komunikasi verbal. Ekspresi wajah berpengaruh
penting dalam menentukan emosi dari manusia. Deteksi ekspresi
wajah secara otomatis sangat penting untuk diaplikasikan, seperti
untuk image understanding, health-care, human computer
interaction, video games dan animasi data-driven. akan tetapi,
deteksi ekspresi wajah dengan akurasi yang tinggi masih menjadi
tantangan terbuka bagi semua peneliti[1].
Kebanyakan sistem analisis pada ekspresi wajah
memfokuskan pada deteksi 6 emosi dasar yang diajukan oleh
Ekman, yaitu : marah, jijik, takut, bahagia, sedih, dan terkejut.
Setiap ekspresi dasar tersebut dapat didekomposisikan menjadi
serangkaian Action Units (AUs) yang berkaitan[1]. Misalnya,
ekspresi bahagia dapat didekomposisikan menjadi pipi yang naik
dan sudut bibir yang tertarik. Bahkan pada studi psikologi klasik
menunjukkan bahwa manusia secara sadar memetakan AUs ke
kategori emosi dasar.
Tugas akhir ini menyajikan sebuah video-based method
untuk menganalisis ekspresi wajah dengan mengenali AU dan
dilakukan pelacakan terhadap titik fitur wajah menggunakan
Active Shape Model (ASM). Vektor perpindahan antara frame ekspresi netral dan frame ekspresi puncak digunakan sebagai fitur
gerakan dari ekspresi wajah. Fitur tersebut diidentifikasi dengan
level pertama klasifier random forest untuk mendeteksi AU. AU
yang terdeteksi kemudian diklasifikasikan kedalam ekspresiekspresi
yang berbeda-beda dengan klasifier random forest level
kedua. Dengan menggunakan data yang diambil dari 3 subjek uji
coba, akurasi terbesar didapatkan dengan nilai parameter
averaging k = 3 dan iterasi twofold random forest = 100 dengan
nilai akurasi sebesar 74.45% dengan 6 kelas ekspresi dan 100%
dengan 5 kelas ekspresi.
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Facial expressions are an important nonverbal
information to supplement verbal communication. Facial
expressions are important in determining the influence of human
emotions. Automatic detection of facial expressions is very
important to be applied, such as for image understanding, healthcare,
human computer interaction, video games and animation
data-driven. however, the detection of facial expressions with high
accuracy remains a challenge open to all researchers[1].
Most system analysis on expression face detection focus on
six basic emotions proposed by Ekman, namely: anger, disgust,
fear, happiness, sadness, and surprise. Each of these basic
expressions can be decomposed into a series of related Action
Units (AUs)[1]. For example, the happy expression can be
decomposed into the cheek that rises and the lip corners are
stretch. Even in a classic psychology study shows that humans
consciously charted AUs to basic emotional categories.
This final project presents a video-based method for
analyzing facial expressions with the AUs to recognize and to track
of facial feature points using Active Shape Model (ASM). The
displacement vector between the frame and the frame of neutral
expression used as the ultimate expression of the movement of the facial expressions. Such a feature is identified by the first level
klasifier random forest to detect the AU. AU is detected then
classified into expressions that vary with random forest klasifier
second level. Using data taken from three subjects test, best
accuracy is obtained with averaging parameter k = 3 and twofold
random forest iteration = 100 with a value 74.45% with 6
expression class and 100% with 5 expression class
Facial expression recognition and intensity estimation.
Doctoral Degree. University of KwaZulu-Natal, Durban.Facial Expression is one of the profound non-verbal channels through which human emotion state is inferred from the deformation or movement of face components when facial muscles are activated. Facial Expression Recognition (FER) is one of the relevant research fields in Computer Vision (CV) and Human-Computer Interraction (HCI). Its application is not limited to: robotics, game, medical, education, security and marketing. FER consists of a wealth of information. Categorising the information into primary emotion states only limit its performance. This thesis considers investigating an approach that simultaneously predicts the emotional state of facial expression images and the corresponding degree of intensity. The task also extends to resolving FER ambiguous nature and annotation inconsistencies with a label distribution learning method that considers correlation among data. We first proposed a multi-label approach for FER and its intensity estimation using advanced machine learning techniques. According to our findings, this approach has not been considered for emotion and intensity estimation in the field before. The approach used problem transformation to present FER as a multilabel task, such that every facial expression image has unique emotion information alongside the corresponding degree of intensity at which the emotion is displayed. A Convolutional Neural Network (CNN) with a sigmoid function at the final layer is the classifier for the model. The model termed ML-CNN (Multilabel Convolutional Neural Network) successfully achieve concurrent prediction of emotion and intensity estimation. ML-CNN prediction is challenged with overfitting and intraclass and interclass variations. We employ Visual Geometric Graphics-16 (VGG-16) pretrained network to resolve the overfitting challenge and the aggregation of island loss and binary cross-entropy loss to minimise the effect of intraclass and interclass variations. The enhanced ML-CNN model shows promising results and outstanding performance than other standard multilabel algorithms. Finally, we approach data annotation inconsistency and ambiguity in FER data using isomap manifold learning with Graph Convolutional Networks (GCN). The GCN uses the distance along the isomap manifold as the edge weight, which appropriately models the similarity between adjacent nodes for emotion predictions. The proposed method produces a promising result in comparison with the state-of-the-art methods.Author's List of Publication is on page xi of this thesis
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