Emotion recognition from dynamic emotional displays following anterior cingulotomy and anterior capsulotomy for chronic depression

Four patients that had received an anterior cingulotomy (ACING) and five patients that had received both an ACING and an anterior capsulotomy (ACAPS) as an intervention for chronic, treatment refractory depression were presented with a series of dynamic emotional stimuli and invited to identify the emotion portrayed. Their performance was compared with that of a group of non-surgically treated patients with major depression (n = 17) and with a group of matched, never-depressed controls (n = 22). At the time of testing, four of the nine neurosurgery patients had recovered from their depressive episode, whereas five remained depressed. Analysis of emotion recognition accuracy revealed no significant differences between depressed and non-depressed neurosurgically treated patients. Similarly, no significant differences were observed between the patients treated with ACING alone and those treated with both ACING and ACAPS. Comparison of the emotion recognition accuracy of the neurosurgically treated patients and the depressed and healthy control groups revealed that the surgically treated patients exhibited a general impairment in their recognition accuracy compared to healthy controls. Regression analysis revealed that participants' emotion recognition accuracy was predicted by the number of errors they made on the Stroop colour-naming task. It is plausible that the observed deficit in emotion recognition accuracy was a consequence of impaired attentional control, which may have been a result of the surgical lesions to the anterior cingulate cortex. © 2007 Elsevier Ltd. All rights reserved.

An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

The effect of language models on phonetic decoding for spoken term detection

Spoken term detection (STD) popularly involves performing word or sub-word level speech recognition and indexing the result. This work challenges the assumption that improved speech recognition accuracy implies better indexing for STD. Using an index derived from phone lattices, this paper examines the effect of language model selection on the relationship between phone recognition accuracy and STD accuracy. Results suggest that language models usually improve phone recognition accuracy but their inclusion does not always translate to improved STD accuracy. The findings suggest that using phone recognition accuracy to measure the quality of an STD index can be problematic, and highlight the need for an alternative that is more closely aligned with the goals of the specific detection task.

Driving-related eye and head movements are changed by the type of presbyopic correction

Purpose: To investigate whether wearing different presbyopic vision corrections alters the pattern of eye and head movements when viewing dynamic driving-related traffic scenes. Methods: Participants included 20 presbyopes (mean age: 56±5.7 years) who had no experience of wearing presbyopic vision corrections (i.e. all were single vision wearers). Eye and head movements were recorded while wearing five different vision corrections: single vision lenses (SV), progressive addition spectacle lenses (PALs), bifocal spectacle lenses (BIF), monovision (MV) and multifocal contact lenses (MTF CL) in random order. Videotape recordings of traffic scenes of suburban roads and expressways (with edited targets) were presented as dynamic driving-related stimuli and digital numeric display panels included as near visual stimuli (simulating speedometer and radio). Eye and head movements were recorded using the faceLAB™ system and the accuracy of target identification was also recorded. Results: The magnitude of eye movements while viewing the driving-related traffic scenes was greater when wearing BIF and PALs than MV and MTF CL (p≤0.013). The magnitude of head movements was greater when wearing SV, BIF and PALs than MV and MTF CL (p<0.0001) and the number of saccades was significantly higher for BIF and PALs than MV (p≤0.043). Target recognition accuracy was poorer for all vision corrections when the near stimulus was located at eccentricities inferiorly and to the left, rather than directly below the primary position of gaze (p=0.008), and PALs gave better performance than MTF CL (p=0.043). Conclusions: Different presbyopic vision corrections alter eye and head movement patterns. In particular, the larger magnitude of eye and head movements and greater number of saccades associated with the spectacle presbyopic corrections, may impact on driving performance.

Discriminative optimisation of the figure of merit for phonetic spoken term detection

This work proposes to improve spoken term detection (STD) accuracy by optimising the Figure of Merit (FOM). In this article, the index takes the form of phonetic posterior-feature matrix. Accuracy is improved by formulating STD as a discriminative training problem and directly optimising the FOM, through its use as an objective function to train a transformation of the index. The outcome of indexing is then a matrix of enhanced posterior-features that are directly tailored for the STD task. The technique is shown to improve the FOM by up to 13% on held-out data. Additional analysis explores the effect of the technique on phone recognition accuracy, examines the actual values of the learned transform, and demonstrates that using an extended training data set results in further improvement in the FOM.

Host selection in phytophagous insects: a new explanation for learning in adults

Insect learning can change the preferences an egg laying female displays towards different host plant species. Current hypotheses propose that learning may be advantageous in adult host selection behaviour through improved recognition, accuracy or selectivity in foraging. In this paper, we present a hypothesis for when learning can be advantageous without such improvements in adult host foraging. Specifically, that learning can be an advantageous strategy for egg laying females when larvae must feed on more than one plant in order to complete development, if the fitness of larvae is reduced when they switch to a different host species. Here, larvae benefit from developing on the most abundant host species, which is the most likely choice of host for an adult insect which increases its preference for a host species through learning. The hypothesis is formalised with a mathematical model and we provide evidence from studies on the behavioural ecology of a number of insect species which demonstrate that the assumptions of this hypothesis may frequently be fulfilled in nature. We discuss how multiple mechanisms may convey advantages in insect learning and that benefits to larval development, which have so far been overlooked, should be considered in explanations for the widespread occurrence of learning.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

Facial emotion processing in schizophrenia : no evidence for a deficit specific to negative emotions in a differential deficit design

People with schizophrenia perform poorly when recognising facial expressions of emotion, particularly negative emotions such as fear. This finding has been taken as evidence of a “negative emotion specific deficit”, putatively associated with a dysfunction in the limbic system, particularly the amygdala. An alternative explanation is that greater difficulty in recognising negative emotions may reflect a priori differences in task difficulty. The present study uses a differential deficit design to test the above argument. Facial emotion recognition accuracy for seven emotion categories was compared across three groups. Eighteen schizophrenia patients and one group of healthy age- and gender-matched controls viewed identical sets of stimuli. A second group of 18 age- and gender-matched controls viewed a degraded version of the same stimuli. The level of stimulus degradation was chosen so as to equate overall level of accuracy to the schizophrenia patients. Both the schizophrenia group and the degraded image control group showed reduced overall recognition accuracy and reduced recognition accuracy for fearful and sad facial stimuli compared with the intact-image control group. There were no differences in recognition accuracy for any emotion category between the schizophrenia group and the degraded image control group. These findings argue against a negative emotion specific deficit in schizophrenia.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Acoustic adaptation in cross database audio visual SHMM training for phonetic spoken term detection

Visual information in the form of lip movements of the speaker has been shown to improve the performance of speech recognition and search applications. In our previous work, we proposed cross database training of synchronous hidden Markov models (SHMMs) to make use of external large and publicly available audio databases in addition to the relatively small given audio visual database. In this work, the cross database training approach is improved by performing an additional audio adaptation step, which enables audio visual SHMMs to benefit from audio observations of the external audio models before adding visual modality to them. The proposed approach outperforms the baseline cross database training approach in clean and noisy environments in terms of phone recognition accuracy as well as spoken term detection (STD) accuracy.

Incorporating visual information for spoken term detection

Spoken term detection (STD) is the task of looking up a spoken term in a large volume of speech segments. In order to provide fast search, speech segments are first indexed into an intermediate representation using speech recognition engines which provide multiple hypotheses for each speech segment. Approximate matching techniques are usually applied at the search stage to compensate the poor performance of automatic speech recognition engines during indexing. Recently, using visual information in addition to audio information has been shown to improve phone recognition performance, particularly in noisy environments. In this paper, we will make use of visual information in the form of lip movements of the speaker in indexing stage and will investigate its effect on STD performance. Particularly, we will investigate if gains in phone recognition accuracy will carry through the approximate matching stage to provide similar gains in the final audio-visual STD system over a traditional audio only approach. We will also investigate the effect of using visual information on STD performance in different noise environments.

A biomechanical approach to iris normalization

The richness of the iris texture and its variability across individuals make it a useful biometric trait for personal authentication. One of the key stages in classical iris recognition is the normalization process, where the annular iris region is mapped to a dimensionless pseudo-polar coordinate system. This process results in a rectangular structure that can be used to compensate for differences in scale and variations in pupil size. Most iris recognition methods in the literature adopt linear sampling in the radial and angular directions when performing iris normalization. In this paper, a biomechanical model of the iris is used to define a novel nonlinear normalization scheme that improves iris recognition accuracy under different degrees of pupil dilation. The proposed biomechanical model is used to predict the radial displacement of any point in the iris at a given dilation level, and this information is incorporated in the normalization process. Experimental results on the WVU pupil light reflex database (WVU-PLR) indicate the efficacy of the proposed technique, especially when matching iris images with large differences in pupil size.

Optimal feature extraction for bilingual OCR

Feature extraction in bilingual OCR is handicapped by the increase in the number of classes or characters to be handled. This is evident in the case of Indian languages whose alphabet set is large. It is expected that the complexity of the feature extraction process increases with the number of classes. Though the determination of the best set of features that could be used cannot be ascertained through any quantitative measures, the characteristics of the scripts can help decide on the feature extraction procedure. This paper describes a hierarchical feature extraction scheme for recognition of printed bilingual (Tamil and Roman) text. The scheme divides the combined alphabet set of both the scripts into subsets by the extraction of certain spatial and structural features. Three features viz geometric moments, DCT based features and Wavelet transform based features are extracted from the grouped symbols and a linear transformation is performed on them for the purpose of efficient representation in the feature space. The transformation is obtained by the maximization of certain criterion functions. Three techniques : Principal component analysis, maximization of Fisher's ratio and maximization of divergence measure have been employed to estimate the transformation matrix. It has been observed that the proposed hierarchical scheme allows for easier handling of the alphabets and there is an appreciable rise in the recognition accuracy as a result of the transformations.

Fast acoustic likelihood computation using low-rank matrix approximation

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for LVCSR systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication.In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on a 1138 word vocabulary RM1 task using Sphinx 3.7 system show that, for a typical case the matrix multiplication approach leads to overall speedup of 46%. Both the low-rank approximation methods increase the speedup to around 60%, with the former method increasing the word error rate (WER) from 3.2% to 6.6%, while the latter increases the WER from 3.2% to 3.5%.

structuring and manipulating hand-drawn concept maps

Concept maps are an important tool to knowledge organization,representation, and sharing. Most current concept map tools do not provide full support for hand-drawn concept map creation and manipulation, largely due to the lack of methods to recognize hand-drawn concept maps. This paper proposes a structure recognition method. Our algorithm can extract node blocks and link blocks of a hand-drawn concept map by combining dynamic programming and graph partitioning and then build a concept-map structure by relating extracted nodes and links. We also introduce structure-based intelligent manipulation technique of hand-drawn concept maps. Evaluation shows that our method has high structure recognition accuracy in real time, and the intelligent manipulation technique is efficient and effective.