1,992 research outputs found
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
Fusion of Multiple Biometric For Photo-Attack Detection in Face Recognition Systems
A spoofing attack is a situation in which one person successfully masquerades as another by falsifying data and gaining illegitimate access. Spoofing attacks are of several types such as photograph, video or mask. Biometrics are playing the role of a password which cannot be replaced if stolen, so there is the necessity of counter-measures to biometric spoofing attacks. Face biometric systems are vulnerable to spoofing attack. Regardless of the biometric mode, the typical approach of anti-spoofing systems is to classify the biometric evidence which are based on features discriminating between real accesses and spoofing attacks. A number of biometric characteristics are in use in various applications. This system will be based on face recognition and lip movement recognition systems. This system will make use of client-specific information to build client-specific anti-spoofing solution, depending on a generative model. In this system, we will implement the client identity to detect spoofing attack. With this, it increases efficiency of authentication. The image will be captured and registered with its client identity. When user has to be authenticated, the image will be captured with his identity manually entered. Now system will check the image with respect to client identity only. Lip movement recognition will be done at time of authentication to identify whether client is spoof or not. If client is authenticated, then it will check for captured image dimension using Gaussian Mixture Model (GMM). This system also encrypts and decrypts a file by extracting parameter values of a registered face
One-shot lip-based biometric authentication: extending behavioral features with authentication phrase information
Lip-based biometric authentication (LBBA) is an authentication method based
on a person's lip movements during speech in the form of video data captured by
a camera sensor. LBBA can utilize both physical and behavioral characteristics
of lip movements without requiring any additional sensory equipment apart from
an RGB camera. State-of-the-art (SOTA) approaches use one-shot learning to
train deep siamese neural networks which produce an embedding vector out of
these features. Embeddings are further used to compute the similarity between
an enrolled user and a user being authenticated. A flaw of these approaches is
that they model behavioral features as style-of-speech without relation to what
is being said. This makes the system vulnerable to video replay attacks of the
client speaking any phrase. To solve this problem we propose a one-shot
approach which models behavioral features to discriminate against what is being
said in addition to style-of-speech. We achieve this by customizing the GRID
dataset to obtain required triplets and training a siamese neural network based
on 3D convolutions and recurrent neural network layers. A custom triplet loss
for batch-wise hard-negative mining is proposed. Obtained results using an
open-set protocol are 3.2% FAR and 3.8% FRR on the test set of the customized
GRID dataset. Additional analysis of the results was done to quantify the
influence and discriminatory power of behavioral and physical features for
LBBA.Comment: 28 pages, 10 figures, 7 table
Real-time speaker identification for video conferencing
Automatic speaker identification in a videoconferencing environment will allow conference attendees to focus their
attention on the conference rather than having to be engaged manually in identifying which channel is active and who
may be the speaker within that channel. In this work we present a real-time, audio-coupled video based approach to
address this problem, but focus more on the video analysis side. The system is driven by the need for detecting a talking
human via the use of computer vision algorithms. The initial stage consists of a face detector which is subsequently
followed by a lip-localization algorithm that segments the lip region. A novel approach for lip movement detection based
on image registration and using the Coherent Point Drift (CPD) algorithm is proposed. Coherent Point Drift (CPD) is a
technique for rigid and non-rigid registration of point sets. We provide experimental results to analyse the performance
of the algorithm when used in monitoring real life videoconferencing data
Face analysis using curve edge maps
This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking
Visual Passwords Using Automatic Lip Reading
This paper presents a visual passwords system to increase security. The
system depends mainly on recognizing the speaker using the visual speech signal
alone. The proposed scheme works in two stages: setting the visual password
stage and the verification stage. At the setting stage the visual passwords
system request the user to utter a selected password, a video recording of the
user face is captured, and processed by a special words-based VSR system which
extracts a sequence of feature vectors. In the verification stage, the same
procedure is executed, the features will be sent to be compared with the stored
visual password. The proposed scheme has been evaluated using a video database
of 20 different speakers (10 females and 10 males), and 15 more males in
another video database with different experiment sets. The evaluation has
proved the system feasibility, with average error rate in the range of 7.63% to
20.51% at the worst tested scenario, and therefore, has potential to be a
practical approach with the support of other conventional authentication
methods such as the use of usernames and passwords
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