69,922 research outputs found
Facial Image Analysis for Body Mass Index, Makeup and Identity
The principal aim of facial image analysis in computer vision is to extract valuable information(e.g., age, gender, ethnicity, and identity) by interpreting perceived electronic signals from face images. In this dissertation, we develop facial image analysis systems for body mass index (BMI) prediction, makeup detection, as well as facial identity with makeup changes and BMI variations.;BMI is a commonly used measure of body fatness. In the first part of this thesis, we study BMI related topics. At first, we develop a computational method to predict BMI information from face images automatically. We formulate the BMI prediction from facial features as a machine vision problem. Three regression methods, including least square estimation, Gaussian processes for regression, and support vector regression are employed to predict the BMI value. Our preliminary results show that it is feasible to develop a computational system for BMI prediction from face images. Secondly, we address the influence of BMI changes on face identity. Both synthesized and real face images are assembled as the databases to facilitate our study. Empirically, we found that large BMI alterations can significantly reduce the matching accuracy of the face recognition system. Then we study if the influence of BMI changes can be reduced to improve the face recognition performance. The partial least squares (PLS) method is applied for this purpose. Experimental results show the feasibility to develop algorithms to address the influence of facial adiposity variations on face recognition, caused by BMI changes.;Makeup can affect facial appearance obviously. In the second part of this thesis, we deal with makeup influence on face identity. It is principal to perform makeup detection at first to address makeup influence. Four categories of features are proposed to characterize facial makeup cues in our study, including skin color tone, skin smoothness, texture, and highlight. A patch selection scheme and discriminative mapping are presented to enhance the performance of makeup detection. Secondly, we study dual attributes from makeup and non-makeup faces separately to reflect facial appearance changes caused by makeup in a semantic level. Cross-makeup attribute classification and accuracy change analysis is operated to divide dual attributes into four categories according to different makeup effects. To develop a face recognition system that is robust to facial makeup, PLS method is proposed on features extracted from local patches. We also propose a dual-attributes based method for face verification. Shared dual attributes can be used to measure facial similarity, rather than a direct matching with low-level features. Experimental results demonstrate the feasibility to eliminate the influence of makeup on face recognition.;In summary, contributions of this dissertation center in developing facial image analysis systems to deal with newly emerged topics effectively, i.e., BMI prediction, makeup detection, and the rcognition of face identity with makeup and BMI changes. In particular,to the best of our knowledge, BMI related topics, i.e., BMI prediction; the influence of BMI changes on face recognition; and face recognition robust to BMI changes are first explorations to the biometrics society
Finger vein verification
At present, biometric system is well
-
liked as its high security level manage to
reduce frauds, intrudes and forgeries. A biometric system utilizes physiological features
and behavior characteristics of an individual such as face, finger mark, iris,
handwriting,
voice, signature and others. One of the recent biology feature used as biometric system is
the finger vein. The vein features are robust, stable and most importantly unique for
every individual. This trait offers a higher security because for
gery is extremely difficult.
The finger vein verification project verifies a person‟s identity based on the vein
patterns. Generally, the finger vein images are pre
-
processed and a neural network
algorithm is developed to verify the finger vein images. Las
t but not least, the
performance of the finger vein verification is evaluated. The project achieved an overall
accuracy of 82.86%
Reference face graph for face recognition
Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation
Video-based driver identification using local appearance face recognition
In this paper, we present a person identification system for vehicular environments. The proposed system uses face images of the driver and utilizes local appearance-based face recognition over the video sequence. To perform local appearance-based face recognition, the input face image is decomposed into non-overlapping blocks and on each local block discrete cosine transform is applied to extract the local features. The extracted local features are then combined to construct the overall feature vector. This process is repeated for each video frame. The distribution of the feature vectors over the video are modelled using a Gaussian distribution function at the training stage. During testing, the feature vector extracted from each frame is compared to each person’s distribution, and individual likelihood scores are generated. Finally, the person is identified as the one who has maximum joint-likelihood score. To assess the performance of the developed system, extensive experiments are conducted on different identification scenarios, such as closed set identification, open set identification and verification. For the experiments a subset of the CIAIR-HCC database, an in-vehicle data corpus that is collected at the Nagoya University, Japan is used. We show that, despite varying environment and illumination conditions, that commonly exist in vehicular environments, it is possible to identify individuals robustly from their face images. Index Terms — Local appearance face recognition, vehicle environment, discrete cosine transform, fusion. 1
On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly
In the field of face recognition, Sparse Representation (SR) has received
considerable attention during the past few years. Most of the relevant
literature focuses on holistic descriptors in closed-set identification
applications. The underlying assumption in SR-based methods is that each class
in the gallery has sufficient samples and the query lies on the subspace
spanned by the gallery of the same class. Unfortunately, such assumption is
easily violated in the more challenging face verification scenario, where an
algorithm is required to determine if two faces (where one or both have not
been seen before) belong to the same person. In this paper, we first discuss
why previous attempts with SR might not be applicable to verification problems.
We then propose an alternative approach to face verification via SR.
Specifically, we propose to use explicit SR encoding on local image patches
rather than the entire face. The obtained sparse signals are pooled via
averaging to form multiple region descriptors, which are then concatenated to
form an overall face descriptor. Due to the deliberate loss spatial relations
within each region (caused by averaging), the resulting descriptor is robust to
misalignment & various image deformations. Within the proposed framework, we
evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder
Neural Network (SANN), and an implicit probabilistic technique based on
Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and
ChokePoint datasets show that the proposed local SR approach obtains
considerably better and more robust performance than several previous
state-of-the-art holistic SR methods, in both verification and closed-set
identification problems. The experiments also show that l1-minimisation based
encoding has a considerably higher computational than the other techniques, but
leads to higher recognition rates
- …