3,020 research outputs found
Recommended from our members
Predictive models for multibiometric systems
Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations
Learnable PINs: Cross-Modal Embeddings for Person Identity
We propose and investigate an identity sensitive joint embedding of face and
voice. Such an embedding enables cross-modal retrieval from voice to face and
from face to voice. We make the following four contributions: first, we show
that the embedding can be learnt from videos of talking faces, without
requiring any identity labels, using a form of cross-modal self-supervision;
second, we develop a curriculum learning schedule for hard negative mining
targeted to this task, that is essential for learning to proceed successfully;
third, we demonstrate and evaluate cross-modal retrieval for identities unseen
and unheard during training over a number of scenarios and establish a
benchmark for this novel task; finally, we show an application of using the
joint embedding for automatically retrieving and labelling characters in TV
dramas.Comment: To appear in ECCV 201
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
BioSecure: white paper for research in biometrics beyond BioSecure
This report is the output of a consultation process of various major stakeholders in the biometric community to identify the future biometrical research issues, an activity which employed not only researchers but representatives from the entire biometrical community, consisting of governments, industry, citizens and academia. It is one of the main efforts of the BioSecure Network of Excellence to define the agenda for future biometrical research, including systems and applications scenarios
Human abnormal behavior impact on speaker verification systems
Human behavior plays a major role in improving human-machine communication. The performance must be affected by abnormal behavior as systems are trained using normal utterances. The abnormal behavior is often associated with a change in the human emotional state. Different emotional states cause physiological changes in the human body that affect the vocal tract. Fear, anger, or even happiness we recognize as a deviation from a normal behavior. The whole spectrum of human-machine application is susceptible to behavioral changes. Abnormal behavior is a major factor, especially for security applications such as verification systems. Face, fingerprint, iris, or speaker verification is a group of the most common approaches to biometric authentication today. This paper discusses human normal and abnormal behavior and its impact on the accuracy and effectiveness of automatic speaker verification (ASV). The support vector machines classifier inputs are Mel-frequency cepstral coefficients and their dynamic changes. For this purpose, the Berlin Database of Emotional Speech was used. Research has shown that abnormal behavior has a major impact on the accuracy of verification, where the equal error rate increase to 37 %. This paper also describes a new design and application of the ASV system that is much more immune to the rejection of a target user with abnormal behavior.Web of Science6401274012
Deep Sketch-Photo Face Recognition Assisted by Facial Attributes
In this paper, we present a deep coupled framework to address the problem of
matching sketch image against a gallery of mugshots. Face sketches have the
essential in- formation about the spatial topology and geometric details of
faces while missing some important facial attributes such as ethnicity, hair,
eye, and skin color. We propose a cou- pled deep neural network architecture
which utilizes facial attributes in order to improve the sketch-photo
recognition performance. The proposed Attribute-Assisted Deep Con- volutional
Neural Network (AADCNN) method exploits the facial attributes and leverages the
loss functions from the facial attributes identification and face verification
tasks in order to learn rich discriminative features in a common em- bedding
subspace. The facial attribute identification task increases the inter-personal
variations by pushing apart the embedded features extracted from individuals
with differ- ent facial attributes, while the verification task reduces the
intra-personal variations by pulling together all the fea- tures that are
related to one person. The learned discrim- inative features can be well
generalized to new identities not seen in the training data. The proposed
architecture is able to make full use of the sketch and complementary fa- cial
attribute information to train a deep model compared to the conventional
sketch-photo recognition methods. Exten- sive experiments are performed on
composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The
results show the superiority of our method compared to the state- of-the-art
models in sketch-photo recognition algorithm
Baseline analysis of a conventional and virtual reality lifelog retrieval system
Continuous media capture via a wearable devices is currently one of the most popular methods to establish a comprehensive record of the entirety of an individual's life experience, referred to in the research community as a lifelog. These vast multimodal corpora include visual and other sensor data and are enriched by content analysis, to generate as extensive a record of an individual's life experience. However, interfacing with such datasets remains an active area of research, and despite the advent of new technology and a plethora of competing mediums for processing digital information, there has been little focus on newly emerging platforms such as virtual reality. In this work, we suggest that the increase in immersion and spatial dimensions provided by virtual reality could provide significant benefits to users when compared to more conventional access methodologies. Hence, we motivate virtual reality as a viable method of exploring multimedia archives (specifically lifelogs) by performing a baseline comparative analysis using a novel application prototype built for the HTC Vive and a conventional prototype built for a standard personal computer
- …