18,178 research outputs found
Face recognition using the Moving Window Classifier
The Moving Window Classifier (MWC) has previously been proposed as an efficient scheme for text recognition applications. In this paper, the potential of the MWC algorithm in face recognition is investigated. To maintain the memory requirements of the classifier within acceptable practical limits, the concept of bit-plane encoding is utilized. The experimental results reported show very encouraging performance for both the schemes
Fast Video Classification via Adaptive Cascading of Deep Models
Recent advances have enabled "oracle" classifiers that can classify across
many classes and input distributions with high accuracy without retraining.
However, these classifiers are relatively heavyweight, so that applying them to
classify video is costly. We show that day-to-day video exhibits highly skewed
class distributions over the short term, and that these distributions can be
classified by much simpler models. We formulate the problem of detecting the
short-term skews online and exploiting models based on it as a new sequential
decision making problem dubbed the Online Bandit Problem, and present a new
algorithm to solve it. When applied to recognizing faces in TV shows and
movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on
GPU/CPU) relative to a state-of-the-art convolutional neural network, at
competitive accuracy.Comment: Accepted at IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
Predicting and improving the recognition of emotions
The technological world is moving towards more effective and friendly human computer interaction. A key factor of these emerging requirements is the ability of future systems to recognise human emotions, since emotional information is an important part of human-human communication and is therefore expected to be essential in natural and intelligent human-computer interaction. Extensive research has been done on emotion recognition using facial expressions, but all of these methods rely mainly on the results of some classifier based on the apparent expressions. However, the results of classifier may be badly affected by the noise including occlusions, inappropriate lighting conditions, sudden movement of head and body, talking, and other possible problems. In this paper, we propose a system using exponential moving averages and Markov chain to improve the classifier results and somewhat predict the future emotions by taking into account the current as well as previous emotions
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Authentication of smartphone users is important because a lot of sensitive
data is stored in the smartphone and the smartphone is also used to access
various cloud data and services. However, smartphones are easily stolen or
co-opted by an attacker. Beyond the initial login, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data. Hence, this paper proposes a novel authentication system for
implicit, continuous authentication of the smartphone user based on behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We propose novel context-based authentication models to
differentiate the legitimate smartphone owner versus other users. We
systematically show how to achieve high authentication accuracy with different
design alternatives in sensor and feature selection, machine learning
techniques, context detection and multiple devices. Our system can achieve
excellent authentication performance with 98.1% accuracy with negligible system
overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable
Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap
with arXiv:1703.0352
"'Who are you?' - Learning person specific classifiers from video"
We investigate the problem of automatically labelling
faces of characters in TV or movie material with their
names, using only weak supervision from automaticallyaligned
subtitle and script text. Our previous work (Everingham
et al. [8]) demonstrated promising results on the
task, but the coverage of the method (proportion of video
labelled) and generalization was limited by a restriction to
frontal faces and nearest neighbour classification.
In this paper we build on that method, extending the coverage
greatly by the detection and recognition of characters
in profile views. In addition, we make the following contributions:
(i) seamless tracking, integration and recognition
of profile and frontal detections, and (ii) a character specific
multiple kernel classifier which is able to learn the features
best able to discriminate between the characters.
We report results on seven episodes of the TV series
“Buffy the Vampire Slayer”, demonstrating significantly increased
coverage and performance with respect to previous
methods on this material
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