John See

Fuzzy edge detector using entropy optimization

This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier f/sub h/ and the crossover point x/sub c/, is used to enhance the image. The entropy function is optimized to obtain the parameters f/sub h/, and x/sub c/ using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, /spl alpha/ and /spl beta/. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.

Efficient spatio-temporal local binary patterns for spontaneous facial micro-expression recognition

Micro-expression recognition is still in the preliminary stage, owing much to the numerous difficulties faced in the development of datasets. Since micro-expression is an important affective clue for clinical diagnosis and deceit analysis, much effort has gone into the creation of these datasets for research purposes. There are currently two publicly available spontaneous micro-expression datasets—SMIC and CASME II, both with baseline results released using the widely used dynamic texture descriptor LBP-TOP for feature extraction. Although LBP-TOP is popular and widely used, it is still not compact enough. In this paper, we draw further inspiration from the concept of LBP-TOP that considers three orthogonal planes by proposing two efficient approaches for feature extraction. The compact robust form described by the proposed LBP-Six Intersection Points (SIP) and a super-compact LBP-Three Mean Orthogonal Planes (MOP) not only preserves the essential patterns, but also reduces the redundancy that affects the discriminality of the encoded features. Through a comprehensive set of experiments, we demonstrate the strengths of our approaches in terms of recognition accuracy and efficiency.

LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition

Facial micro-expression recognition is an upcoming area in computer vision research. Up until the recent emergence of the extensive CASMEII spontaneous micro-expression database, there were numerous obstacles faced in the elicitation and labeling of data involving facial micro-expressions. In this paper, we propose the Local Binary Patterns with Six Intersection Points (LBP-SIP) volumetric descriptor based on the three intersecting lines crossing over the center point. The proposed LBP-SIP reduces the redundancy in LBP-TOP patterns, providing a more compact and lightweight representation; leading to more efficient computational complexity. Furthermore, we also incorporated a Gaussian multi-resolution pyramid to our proposed approach by concatenating the patterns across all pyramid levels. Using an SVM classifier with leave-one-sample-out cross validation, we achieve the best recognition accuracy of 67.21 %, surpassing the baseline performance with further computational efficiency.

Subtle Expression Recognition Using Optical Strain Weighted Features

Optical strain characterizes the relative amount of displacement by a moving object within a time interval. Its ability to compute any small muscular movements on faces can be advantageous to subtle expression research. This paper proposes a novel optical strain weighted feature extraction scheme for subtle facial micro-expression recognition. Motion information is derived from optical strain magnitudes, which is then pooled spatio-temporally to obtain block-wise weights for the spatial image plane. By simple product with the weights, the resulting feature histograms are intuitively scaled to accommodate the importance of block regions. Experiments conducted on two recent spontaneous micro-expression databases–CASMEII and SMIC, demonstrated promising improvement over the baseline results.

An integrated vision-based architecture for home security system

Automated security systems are a useful addition to todays home where safety is an important issue. Vision-based security systems have the advantage of being easy to set up, inexpensive and non-obtrusive. This paper proposes an integrated dual-level vision-based home security system, which consists of two subsystems - a face recognition module and a motion detection module. The primary face recognition module functions as a user authentication device. On an event of a failure in the primary system, the secondary motion detection module acts as a reliable backup to detect human-related motions within certain locations inside the home. Novel algorithms have been proposed for both subsystems. Several experiments and field tests conducted have shown good performance and feasible implementation in both subsystems.

Optical strain based recognition of subtle emotions

This paper presents a novel method to recognize subtle emotions based on optical strain magnitude feature extraction from the temporal point of view. The common way that subtle emotions are exhibited by a person is in the form of visually observed micro-expressions, which usually occur only over a brief period of time. Optical strain allows small deformations on the face to be computed between successive frames although these subtle changes can be minute. We perform temporal sum pooling for each frame in the video to a single strain map to summarize the features over time. To reduce the dimensionality of the input space, the strain maps are then resized to a pre-defined resolution for consistency across the database. Experiments were conducted on the SMIC (Spontaneous Micro-expression) Database, which was recently established in 2013. A best three-class recognition accuracy of 53.56% is achieved, with the proposed method outperforming the baseline reported in the original work by almost 5%. This is the first known optical strain based classification of micro-expressions. The closest related work employed optical strain to spot micro-expressions, but did not investigate its potential for determining the specific type of micro-expression.

Exemplar extraction using spatio-temporal hierarchical agglomerative clustering for face recognition in video

Many recent works have attempted to improve object recognition by exploiting temporal dynamics, an intrinsic property of video sequences. In this paper, a new spatio-temporal hierarchical agglomerative clustering (STHAC) method is proposed for automatic extraction of face exemplars for face recognition in video sequences. Two variants of STHAC are presented — a global variety that unifies spatial and temporal distances between points, and a local variety that introduces perturbation of distances based on a local spatio-temporal neighborhood criterion. Faces that are nearest to the cluster means are chosen as exemplars for the testing stage, where subjects in the test video sequences are recognized using a probabilistic-based classifier. Extensive evaluation on a face video database demonstrates the effectiveness of our proposed method, and the significance of incorporating temporal information for exemplar extraction.

Spontaneous Subtle Expression Recognition: Imbalanced Databases and Solutions

Facial expression analysis has been well studied in recent years; however, these mainly focus on domains of posed or clear facial expressions. Meanwhile, subtle/micro-expressions are rarely analyzed, due to three main difficulties: inter-class similarity (hardly discriminate facial expressions of two subtle emotional states from a person), intra-class dissimilarity (different facial morphology and behaviors of two subjects in one subtle emotion state), and imbalanced sample distribution for each class and subject. This paper aims to solve the last two problems by first employing preprocessing steps: facial registration, cropping and interpolation; and proposes a person-specific AdaBoost classifier with Selective Transfer Machine framework. While preprocessing techniques remove morphological facial differences, the proposed variant of AdaBoost deals with imbalanced characteristics of available subtle expression databases. Performance metrics obtained from experiments on the SMIC and CASME2 spontaneous subtle expression databases confirm that the proposed method improves classification of subtle emotions.

Monogenic Riesz wavelet representation for micro-expression recognition

A monogenic signal is a two-dimensional analytical signal that provides the local information of magnitude, phase, and orientation. While it has been applied on the field of face and expression recognition [1], [2], [3], there are no known usages for subtle facial micro-expressions. In this paper, we propose a feature representation method which succinctly captures these three low-level components at multiple scales. Riesz wavelet transform is employed to obtain multi-scale monogenic wavelets, which are formulated by quaternion representation. Instead of summing up the multi-scale monogenic representations, we consider all monogenic representations across multiple scales as individual features. For classification, two schemes were applied to integrate these multiple feature representations: a fusion-based method which combines the features efficiently and discriminately using the ultra-fast, optimized Multiple Kernel Learning (UFO-MKL) algorithm; and concatenation-based method where the features are combined into a single feature vector and classified by a linear SVM. Experiments carried out on a recent spontaneous micro-expression database demonstrated the capability of the proposed method in outperforming the state-of-the-art monogenic signal approach to solving the micro-expression recognition problem.

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. HighlightsThe method proposed is a combination of two optical strain derived features.Optical strain magnitudes were employed to describe fine subtle facial movements.Evaluation was performed in both the detection and recognition tasks.Promising performances were obtained in two micro-expression databases.