Inter-session variability modelling and joint factor analysis for face authentication

Wallace, Roy; McLaren, Mitchell; McCool, Chris; Marcel, Sébastien

doi:10.1109/ijcb.2011.6117599

Cited by 52 publications

(84 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental protocol followed in this paper is similar to the one proposed in [32] 1 . The database was divided into 3 subsets based on the subject ID: development (1-43), SVM training (44-87), and test (88-130).…”

Section: Experimental Protocolsmentioning

confidence: 99%

Identification using face regions: Application and assessment in forensic scenarios

Tomé

Fiérrez

Vera-Rodríguez

et al. 2013

Forensic Science International

View full text Add to dashboard Cite

El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription AbstractThis paper reports an exhaustive analysis of the discriminative power of the different regions of the human face on various forensic scenarios. In practice, when forensic examiners compare two face images, they focus their attention not only on the overall similarity of the two faces. They carry out an exhaustive morphological comparison region by region (e.g., nose, mouth, eyebrows, etc.). In this scenario it is very important to know based on scientific methods to what extent each facial region can help in identifying a person. This knowledge obtained using quantitative and statical methods on given populations can then be used by the examiner to support or tune his observations. In order to generate such scientific knowledge useful for the expert, several methodologies are compared, such as manual and automatic facial landmarks extraction, different facial regions extractors, and various distances between the subject and the acquisition camera. Also, three scenarios of interest for forensics are considered comparing mugshot and Closed-Circuit TeleVision (CCTV) face images using MORPH and SCface databases. One of the findings is that depending of the acquisition distances, the discriminative power of the facial regions change, having in some cases better performance than the full face.

show abstract

Section: Experimental Protocolsmentioning

confidence: 99%

Identification using face regions: Application and assessment in forensic scenarios

Tomé

Fiérrez

Vera-Rodríguez

et al. 2013

Forensic Science International

View full text Add to dashboard Cite

show abstract

“…For a given client, however, enrollment set typically contains images captured in different sessions, with varying pose, illumination, and facial expressions. This within-class variability for client i is also reflected in s i , and can result in diminished recognition accuracy of the GMM-based FR method [3].…”

Section: Gmm-based Fr Using Inter-session Variability Modelingmentioning

confidence: 99%

“…We have used the ISV modeling approach proposed by Wallace et al [3]. Among the FR methods included in this study, this is the only method that adopts a parts based approach.…”

Section: Gmm-based Fr Using Inter-session Variability Modelingmentioning

confidence: 99%

“…The subspace U is estimated using an expectation-maximization (EM) algorithm. For details of the ISV technique for face-verification, please refer to the works of Wallace et al [3] and Vogt et al [30]. When enrolling a new client, i, using a set of enrollment images (indexed by j), the latent variables x i,j and z i are estimated from the enrollment images, and finally, the client-specific supervector, c i , is computed as:…”

Section: Gmm-based Fr Using Inter-session Variability Modelingmentioning

confidence: 99%

“…The first generation of large scale appearance based FR systems, such as Eigenfaces [1] and Fisherfaces [2], attempted to model the face-variability in a simple linear sub-space. Subsequently, methods such as joint factor analysis (JFA), inter-session variability (ISV) modeling [3] and probabilistic linear discriminant analysis (PLDA) [4,5] were developed to better model variability in face-images. Deep-learning based methods, notably convolutional neural networks (CNN) [6,7,8,9], have become very popular in recent years, due to their near-perfect recognition accuracy on unconstrained datasets such as 'labeled faces in the wild' (LFW) [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deeply vulnerable: a study of the robustness of face recognition to presentation attacks

2017

View full text Add to dashboard Cite

The vulnerability of deep-learning based face-recognition (FR) methods, to presentation attacks (PA) is studied in this paper. Recently proposed FR methods based on deep neural networks (DNN) have been shown to outperform most other methods by a significant margin. In a trustworthy face-verification system, however, maximizing recognition-performance alone is not sufficient -the system should also be capable of resisting various kinds of attacks, including presentation-attacks (PA). Previous experience has shown that the PA-vulnerability of FR systems tends to increase with face-verification accuracy. Using several publicly available PA datasets, we show that DNN-based FR systems compensate for variability between bona fide and PA samples, and tend to score them similarly, which makes such FR systems extremely vulnerable to PAs. Experiments show the vulnerability of the studied DNN-based FR systems to be consistently higher than 90%, and often higher than 98%.

show abstract

LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities

Medapati¹,

Murthy²,

Sridhar

2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Digital transformation played a vital role in smart cities because of its ability to process the different data to providing sustainability, connectivity, and mobility to data effectively. According to the diver operational productivity, preserving public safety government law enforcement integrated that face recognition is more important in smart cities. Traditional face recognition system fails to predict the exact facial features that leads to reduce the facial recognition accuracy. The false facial point detection process maximizes the computation complexity. Therefore, in this work, effective and artificial intelligence internet of things-based facial expression detection system is implemented to predict and match the face from the database. Initially, the facial images are captured from the internet of things sensor device, which is processed by applying the Perona-Malik diffusion algorithm. Then, face location is cropped from the image, geometric face shape model is created for predicting the exact face from the template face image. From the face shape, different facial features are extracted from various region using Fisher linear discriminant analysis. The derived features are trained with the help of convolution network and the face recognition process is done by using the adaboost large memory storage and retrieval neural network. The network successfully recognizes the face from the template, which is used to eliminate the safety related risk in smart cities.

show abstract

Inter-session variability modelling and joint factor analysis for face authentication

Cited by 52 publications

References 18 publications

Identification using face regions: Application and assessment in forensic scenarios

Identification using face regions: Application and assessment in forensic scenarios

Deeply vulnerable: a study of the robustness of face recognition to presentation attacks

LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities

Contact Info

Product

Resources

About