14 research outputs found
Recognizing Surgically Altered Face Images and 3D Facial Expression Recognition
AbstractAltering Facial appearances using surgical procedures are common now days. But it raised challenges for face recognition algorithms. Plastic surgery introduces non linear variations. Because of these variations it is difficult to be modeled by the existing face recognition system. Here presents a multi objective evolutionary granular algorithm. It operates on several granules extracted from a face images at multiple level of granularity. This granular information is unified in an evolutionary manner using multi objective genetic approach. Then identify the facial expression from the face images. For that 3D facial shapes are considering here. A novel automatic feature selection method is proposed based on maximizing the average relative entropy of marginalized class-conditional feature distributions and apply it to a complete pool of candidate features composed of normalized Euclidian distances between 83 facial feature points in the 3D space. A regularized multi-class AdaBoost classification algorithm is used here to get the highest average recognition rate
Neural network method of dynamic biometrics for detecting the substitution of computer
© 2018, Institute of Advanced Scientific Research, Inc.. All rights reserved. In this paper, we research the dynamic neural network method for biometric identification of computer users. We analyze the task of detecting the substitution of computer systems users basing the methods of password authentication or authentication using technical devices. To solve this problem, the need to apply biometric authentication methods is actualized. Various methods of users biometric features isolating based on discrete orthogonal transformations are considered. The requirements for choosing biometric identification and authentication methods are formulated: there is no need for additional hardware equipment, the possibility of imperceptible user identification and the analyzed features readability during the workstation use. According on these requirements necessity of users’ recognition based on the mouse moves dynamics is justified. The technique of initial data collecting and their preparation for analysis on the basis of neural network training is described. The neural network model construction use “Deductor” environment. The method of informative features optimal system and neural network architecture searching is developed. We suggested the most efficiency neural network model by the obtained analysis results for computer user’s biometric identification. As the criterion of neural network model optimality the minimal error in user substitution detecting was chosen. The best was a neural network with 6 neurons in the hidden layer, a binary output and 10 input neurons
An improved age invariant face recognition using data augmentation
In spite of the significant advancement in face recognition expertise, accurately recognizing the face of the same individual across different ages still remains an open research question. Face aging causes intra-subject variations (such as geometric changes during childhood & adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Over the years, researchers have devised different
techniques to improve the accuracy of age invariant face recognition (AIFR) systems. In this paper, the face and gesture recognition network (FG-NET) aging dataset was adopted to enable the benchmarking of experimental
results. The FG-Net dataset was augmented by adding four different types of noises at the preprocessing phase in order to improve the trait aging face features extraction and the training model used at the classification stages,
thus addressing the problem of few available training aging for face recognition dataset. The developed model was an adaptation of a pre-trained convolution neural network architecture (Inception-ResNet-v2) which is a very robust noise. The proposed model on testing achieved a 99.94%
recognition accuracy, a mean square error of 0.0158 and a mean absolute error of 0.0637. The results obtained are significant improvements in comparison with related works
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Pattern mining approaches used in sensor-based biometric recognition: a review
Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems
Beyond Disentangled Representations: An Attentive Angular Distillation Approach to Large-scale Lightweight Age-Invariant Face Recognition
Disentangled representations have been commonly adopted to Age-invariant Face
Recognition (AiFR) tasks. However, these methods have reached some limitations
with (1) the requirement of large-scale face recognition (FR) training data
with age labels, which is limited in practice; (2) heavy deep network
architecture for high performance; and (3) their evaluations are usually taken
place on age-related face databases while neglecting the standard large-scale
FR databases to guarantee its robustness. This work presents a novel Attentive
Angular Distillation (AAD) approach to Large-scale Lightweight AiFR that
overcomes these limitations. Given two high-performance heavy networks as
teachers with different specialized knowledge, AAD introduces a learning
paradigm to efficiently distill the age-invariant attentive and angular
knowledge from those teachers to a lightweight student network making it more
powerful with higher FR accuracy and robust against age factor. Consequently,
AAD approach is able to take the advantages of both FR datasets with and
without age labels to train an AiFR model. Far apart from prior distillation
methods mainly focusing on accuracy and compression ratios in closed-set
problems, our AAD aims to solve the open-set problem, i.e. large-scale face
recognition. Evaluations on LFW, IJB-B and IJB-C Janus, AgeDB and
MegaFace-FGNet with one million distractors have demonstrated the efficiency of
the proposed approach. This work also presents a new longitudinal face aging
(LogiFace) database for further studies in age-related facial problems in
future.Comment: arXiv admin note: substantial text overlap with arXiv:1905.1062
Proof-of-Concept
Biometry is an area in great expansion and is considered as possible solution to cases where high
authentication parameters are required. Although this area is quite advanced in theoretical
terms, using it in practical terms still carries some problems. The systems available still depend
on a high cooperation level to achieve acceptable performance levels, which was the backdrop
to the development of the following project. By studying the state of the art, we propose the
creation of a new and less cooperative biometric system that reaches acceptable performance
levels.A constante necessidade de parâmetros mais elevados de segurança, nomeadamente ao nível
de autenticação, leva ao estudo biometria como possível solução. Actualmente os mecanismos
existentes nesta área tem por base o conhecimento de algo que se sabe ”password” ou algo
que se possui ”codigo Pin”. Contudo este tipo de informação é facilmente corrompida ou contornada.
Desta forma a biometria é vista como uma solução mais robusta, pois garante que a
autenticação seja feita com base em medidas físicas ou compartimentais que definem algo que
a pessoa é ou faz (”who you are” ou ”what you do”).
Sendo a biometria uma solução bastante promissora na autenticação de indivíduos, é cada vez
mais comum o aparecimento de novos sistemas biométricos. Estes sistemas recorrem a medidas
físicas ou comportamentais, de forma a possibilitar uma autenticação (reconhecimento) com
um grau de certeza bastante considerável. O reconhecimento com base no movimento do corpo
humano (gait), feições da face ou padrões estruturais da íris, são alguns exemplos de fontes
de informação em que os sistemas actuais se podem basear. Contudo, e apesar de provarem
um bom desempenho no papel de agentes de reconhecimento autónomo, ainda estão muito
dependentes a nível de cooperação exigida. Tendo isto em conta, e tudo o que já existe no
ramo do reconhecimento biometrico, esta área está a dar passos no sentido de tornar os seus
métodos o menos cooperativos poss??veis. Possibilitando deste modo alargar os seus objectivos
para além da mera autenticação em ambientes controlados, para casos de vigilância e controlo
em ambientes não cooperativos (e.g. motins, assaltos, aeroportos).
É nesta perspectiva que o seguinte projecto surge. Através do estudo do estado da arte, pretende
provar que é possível criar um sistema capaz de agir perante ambientes menos cooperativos,
sendo capaz de detectar e reconhecer uma pessoa que se apresente ao seu alcance.O
sistema proposto PAIRS (Periocular and Iris Recognition Systema) tal como nome indica, efectua
o reconhecimento através de informação extraída da íris e da região periocular (região circundante
aos olhos). O sistema é construído com base em quatro etapas: captura de dados,
pré-processamento, extração de características e reconhecimento. Na etapa de captura de
dados, foi montado um dispositivo de aquisição de imagens com alta resolução com a capacidade
de capturar no espectro NIR (Near-Infra-Red). A captura de imagens neste espectro tem
como principal linha de conta, o favorecimento do reconhecimento através da íris, visto que
a captura de imagens sobre o espectro visível seria mais sensível a variações da luz ambiente.
Posteriormente a etapa de pré-processamento implementada, incorpora todos os módulos do
sistema responsáveis pela detecção do utilizador, avaliação de qualidade de imagem e segmentação
da íris. O modulo de detecção é responsável pelo desencadear de todo o processo, uma
vez que esta é responsável pela verificação da exist?ncia de um pessoa em cena. Verificada
a sua exist?ncia, são localizadas as regiões de interesse correspondentes ? íris e ao periocular,
sendo também verificada a qualidade com que estas foram adquiridas. Concluídas estas
etapas, a íris do olho esquerdo é segmentada e normalizada. Posteriormente e com base em
vários descritores, é extraída a informação biométrica das regiões de interesse encontradas,
e é criado um vector de características biométricas. Por fim, é efectuada a comparação dos
dados biometricos recolhidos, com os já armazenados na base de dados, possibilitando a criação
de uma lista com os níveis de semelhança em termos biometricos, obtendo assim um resposta
final do sistema. Concluída a implementação do sistema, foi adquirido um conjunto de imagens capturadas através do sistema implementado, com a participação de um grupo de voluntários.
Este conjunto de imagens permitiu efectuar alguns testes de desempenho, verificar e afinar
alguns parâmetros, e proceder a optimização das componentes de extração de características e
reconhecimento do sistema. Analisados os resultados foi possível provar que o sistema proposto
tem a capacidade de exercer as suas funções perante condições menos cooperativas