38 research outputs found
Less is More: Micro-expression Recognition from Video using Apex Frame
Despite recent interest and advances in facial micro-expression research,
there is still plenty room for improvement in terms of micro-expression
recognition. Conventional feature extraction approaches for micro-expression
video consider either the whole video sequence or a part of it, for
representation. However, with the high-speed video capture of micro-expressions
(100-200 fps), are all frames necessary to provide a sufficiently meaningful
representation? Is the luxury of data a bane to accurate recognition? A novel
proposition is presented in this paper, whereby we utilize only two images per
video: the apex frame and the onset frame. The apex frame of a video contains
the highest intensity of expression changes among all frames, while the onset
is the perfect choice of a reference frame with neutral expression. A new
feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to
encode essential expressiveness of the apex frame. We evaluated the proposed
method on five micro-expression databases: CAS(ME), CASME II, SMIC-HS,
SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with
our proposed technique achieving a state-of-the-art F1-score recognition
performance of 61% and 62% in the high frame rate CASME II and SMIC-HS
databases respectively.Comment: 14 pages double-column, author affiliations updated, acknowledgment
of grant support adde
On the Security Risk of Cancelable Biometrics
Over the years, a number of biometric template protection schemes, primarily
based on the notion of "cancelable biometrics" (CB) have been proposed. An
ideal cancelable biometric algorithm possesses four criteria, i.e.,
irreversibility, revocability, unlinkability, and performance preservation.
Cancelable biometrics employed an irreversible but distance preserving
transform to convert the original biometric templates to the protected
templates. Matching in the transformed domain can be accomplished due to the
property of distance preservation. However, the distance preservation property
invites security issues, which are often neglected. In this paper, we analyzed
the property of distance preservation in cancelable biometrics, and
subsequently, a pre-image attack is launched to break the security of
cancelable biometrics under the Kerckhoffs's assumption, where the cancelable
biometrics algorithm and parameters are known to the attackers. Furthermore, we
proposed a framework based on mutual information to measure the information
leakage incurred by the distance preserving transform, and demonstrated that
information leakage is theoretically inevitable. The results examined on face,
iris, and fingerprint revealed that the risks origin from the matching score
computed from the distance/similarity of two cancelable templates jeopardize
the security of cancelable biometrics schemes greatly. At the end, we discussed
the security and accuracy trade-off and made recommendations against pre-image
attacks in order to design a secure biometric system.Comment: Submit to P
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain
Optical strain is an extension of optical flow that is capable of quantifying
subtle changes on faces and representing the minute facial motion intensities
at the pixel level. This is computationally essential for the relatively new
field of spontaneous micro-expression, where subtle expressions can be
technically challenging to pinpoint. In this paper, we present a novel method
for detecting and recognizing micro-expressions by utilizing facial optical
strain magnitudes to construct optical strain features and optical strain
weighted features. The two sets of features are then concatenated to form the
resultant feature histogram. Experiments were performed on the CASME II and
SMIC databases. We demonstrate on both databases, the usefulness of optical
strain information and more importantly, that our best approaches are able to
outperform the original baseline results for both detection and recognition
tasks. A comparison of the proposed method with other existing spatio-temporal
feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to
Signal Processing: Image Communication journa
Fast recovery of unknown coefficients in DCT-transformed images
The advancement of cryptography and cryptanalysis has driven numerous innovations over years. Among them is the treatment of cryptanalysis on selectively encrypted content as a recovery problem. Recent research has shown that linear programming is a powerful tool to recover unknown coefficients in DCT-transformed images. While the time complexity is polynomial, it is still too high for large images so faster methods are still desired. In this paper, we propose a fast hierarchical DCT coefficients recovery method by combining image segmentation and linear programming. In theory the proposed method can reduce the overall time complexity by a linear factor which is the number of image segments used. Our experimental results showed that, for 100 test images of different sizes and using a naive image segmentation method based on Otsu’s thresholding algorithm, the proposed method is faster for more than 92% cases and the maximum improvement observed is more than 19 times faster. While being mostly faster, results also showed that the proposed method can roughly maintain the visual quality of recovered images in both objective and subjective terms
PhishWHO: Phishing webpage detection via identity keywords extraction and target domain name finder
This paper proposes a phishing detection technique based on the difference between the target and actual
identities of a webpage. The proposed phishing detection approach, called PhishWHO, can be divided
into three phases. The first phase extracts identity keywords from the textual contents of the website,
where a novel weighted URL tokens system based on the N-gram model is proposed. The second phase
finds the target domain name by using a search engine, and the target domain name is selected based on
identity-relevant features. In the final phase, a 3-tier identity matching system is proposed to determine
the legitimacy of the query webpage. The overall experimental results suggest that the proposed system
outperforms the conventional phishing detection methods considered
Dominant speaker detection in multipoint video communication using Markov chain with non-linear weights and dynamic transition window
This paper proposes an enhanced discrete-time Markov chain algorithm in predicting dominant speaker(s) for multipoint video communication system in the presence of transient speech. The proposed algorithm exploits statistical properties of the past speech patterns to accurately predict the dominant speaker for the next time state. Non-linear weights-based coefficients are employed in the enhanced Markov chain for both the initial state vector and transition probability matrix. These weights significantly improve the time taken to predict a new dominant speaker during a conference session. In addition, a mechanism to dynamically modify the size of the transition probability matrix window/container is introduced to improve the adaptability of the Markov chain towards the variability of speech characteristics. Simulation results indicate that for an 11 conference participants test scenario, the enhanced Markov chain prediction algorithm registered an 85% accuracy in predicting a dominant speaker when compared to an ideal case where there is no transient speech. Misclassification of dominant speakers due to transient speech was also reduced by 87%
Improved coefficient recovery and its application for rewritable data embedding
JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate–distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage to the storage device. To address this problem, various coefficient recovery methods have been proposed in the past, including a divide-and-conquer approach to speed up the recovery process. However, the segmentation technique considered in the existing method operates with the assumption of a bi-modal distribution for the pixel values, but most images do not satisfy this condition. Therefore, in this work, an adaptive method was employed to perform more accurate segmentation, so that the real potential of the previous coefficient recovery methods can be unleashed. In addition, an improved rewritable adaptive data embedding method is also proposed that exploits the recoverability of coefficients. Discrete cosine transformation (DCT) patches and blocks for data hiding are judiciously selected based on the predetermined precision to control the embedding capacity and image distortion. Our results suggest that the adaptive coefficient recovery method is able to improve on the conventional method up to 27% in terms of CPU time, and it also achieved better image quality with most considered images. Furthermore, the proposed rewritable data embedding method is able to embed 20,146 bits into an image of dimensions 512×512