Evaluation of image resolution and super-resolution on face recognition performance

While researchers strive to improve automatic face recognition performance, the relationship between image resolution and face recognition performance has not received much attention. This relationship is examined systematically and a framework is developed such that results from super-resolution techniques can be compared. Three super-resolution techniques are compared with the Eigenface and Elastic Bunch Graph Matching face recognition engines. Parameter ranges over which these techniques provide better recognition performance than interpolated images is determined.

PLDA based speaker recognition on short utterances

This paper investigates the effects of limited speech data in the context of speaker verification using a probabilistic linear discriminant analysis (PLDA) approach. Being able to reduce the length of required speech data is important to the development of automatic speaker verification system in real world applications. When sufficient speech is available, previous research has shown that heavy-tailed PLDA (HTPLDA) modeling of speakers in the i-vector space provides state-of-the-art performance, however, the robustness of HTPLDA to the limited speech resources in development, enrolment and verification is an important issue that has not yet been investigated. In this paper, we analyze the speaker verification performance with regards to the duration of utterances used for both speaker evaluation (enrolment and verification) and score normalization and PLDA modeling during development. Two different approaches to total-variability representation are analyzed within the PLDA approach to show improved performance in short-utterance mismatched evaluation conditions and conditions for which insufficient speech resources are available for adequate system development. The results presented within this paper using the NIST 2008 Speaker Recognition Evaluation dataset suggest that the HTPLDA system can continue to achieve better performance than Gaussian PLDA (GPLDA) as evaluation utterance lengths are decreased. We also highlight the importance of matching durations for score normalization and PLDA modeling to the expected evaluation conditions. Finally, we found that a pooled total-variability approach to PLDA modeling can achieve better performance than the traditional concatenated total-variability approach for short utterances in mismatched evaluation conditions and conditions for which insufficient speech resources are available for adequate system development.

Visual route recognition with a handful of bits

In this paper we use a sequence-based visual localization algorithm to reveal surprising answers to the question, how much visual information is actually needed to conduct effective navigation? The algorithm actively searches for the best local image matches within a sliding window of short route segments or 'sub-routes', and matches sub-routes by searching for coherent sequences of local image matches. In contract to many existing techniques, the technique requires no pre-training or camera parameter calibration. We compare the algorithm's performance to the state-of-the-art FAB-MAP 2.0 algorithm on a 70 km benchmark dataset. Performance matches or exceeds the state of the art feature-based localization technique using images as small as 4 pixels, fields of view reduced by a factor of 250, and pixel bit depths reduced to 2 bits. We present further results demonstrating the system localizing in an office environment with near 100% precision using two 7 bit Lego light sensors, as well as using 16 and 32 pixel images from a motorbike race and a mountain rally car stage. By demonstrating how little image information is required to achieve localization along a route, we hope to stimulate future 'low fidelity' approaches to visual navigation that complement probabilistic feature-based techniques.

A toolbox for animal call recognition

Monitoring the natural environment is increasingly important as habit degradation and climate change reduce theworld’s biodiversity.We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales.One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal’s proximity to the microphone. Second, initial experimentation suggested that no single recognizer could dealwith the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and expertise to prepare. Consequently we adopt bootstrap techniques that can be initiated with little data and refined subsequently. In this paper, we describe our recognition tools and present results for real ecological problems.

Towards robust night and day place recognition using visible and thermal imaging

The chief challenge facing persistent robotic navigation using vision sensors is the recognition of previously visited locations under different lighting and illumination conditions. The majority of successful approaches to outdoor robot navigation use active sensors such as LIDAR, but the associated weight and power draw of these systems makes them unsuitable for widespread deployment on mobile robots. In this paper we investigate methods to combine representations for visible and long-wave infrared (LWIR) thermal images with time information to combat the time-of-day-based limitations of each sensing modality. We calculate appearance-based match likelihoods using the state-of-the-art FAB-MAP [1] algorithm to analyse loop closure detection reliability across different times of day. We present preliminary results on a dataset of 10 successive traverses of a combined urban-parkland environment, recorded in 2-hour intervals from before dawn to after dusk. Improved location recognition throughout an entire day is demonstrated using the combined system compared with methods which use visible or thermal sensing alone.

Towards spontaneous facial expression recognition in real-world video

Facial expression is an important channel of human social communication. Facial expression recognition (FER) aims to perceive and understand emotional states of humans based on information in the face. Building robust and high performance FER systems that can work in real-world video is still a challenging task, due to the various unpredictable facial variations and complicated exterior environmental conditions, as well as the difficulty of choosing a suitable type of feature descriptor for extracting discriminative facial information. Facial variations caused by factors such as pose, age, gender, race and occlusion, can exert profound influence on the robustness, while a suitable feature descriptor largely determines the performance. Most present attention on FER has been paid to addressing variations in pose and illumination. No approach has been reported on handling face localization errors and relatively few on overcoming facial occlusions, although the significant impact of these two variations on the performance has been proved and highlighted in many previous studies. Many texture and geometric features have been previously proposed for FER. However, few comparison studies have been conducted to explore the performance differences between different features and examine the performance improvement arisen from fusion of texture and geometry, especially on data with spontaneous emotions. The majority of existing approaches are evaluated on databases with posed or induced facial expressions collected in laboratory environments, whereas little attention has been paid on recognizing naturalistic facial expressions on real-world data. This thesis investigates techniques for building robust and high performance FER systems based on a number of established feature sets. It comprises of contributions towards three main objectives: (1) Robustness to face localization errors and facial occlusions. An approach is proposed to handle face localization errors and facial occlusions using Gabor based templates. Template extraction algorithms are designed to collect a pool of local template features and template matching is then performed to covert these templates into distances, which are robust to localization errors and occlusions. (2) Improvement of performance through feature comparison, selection and fusion. A comparative framework is presented to compare the performance between different features and different feature selection algorithms, and examine the performance improvement arising from fusion of texture and geometry. The framework is evaluated for both discrete and dimensional expression recognition on spontaneous data. (3) Evaluation of performance in the context of real-world applications. A system is selected and applied into discriminating posed versus spontaneous expressions and recognizing naturalistic facial expressions. A database is collected from real-world recordings and is used to explore feature differences between standard database images and real-world images, as well as between real-world images and real-world video frames. The performance evaluations are based on the JAFFE, CK, Feedtum, NVIE, Semaine and self-collected QUT databases. The results demonstrate high robustness of the proposed approach to the simulated localization errors and occlusions. Texture and geometry have different contributions to the performance of discrete and dimensional expression recognition, as well as posed versus spontaneous emotion discrimination. These investigations provide useful insights into enhancing robustness and achieving high performance of FER systems, and putting them into real-world applications.

Not a dirty word : arts entrepreneurship and higher education

While the majority of creative, performing, and literary artists are self-employed, relatively few tertiary arts schools attempt to develop capabilities for venture creation and management (and entrepreneurship more broadly) and still fewer do so effectively. This article asks why this is the case. It addresses underlying conceptual and philosophical issues encountered by arts educators, arguing that in all three senses of the term: new venture creation; career self-management; and being enterprising, entrepreneurship is essential to career success in the arts. However, the practice of entrepreneurship in the arts is significantly different from the practice of entrepreneurship in business, in terms of the artist’s drivers and aims, as well as the nature of entrepreneurial opportunities, contexts and processes. These differences mean that entrepreneurship curricula cannot simply be imported from Business schools. This article also examines the arts-idiosyncratic challenge of negotiating distinctive and potentially conflicting entrepreneurial aims, using career identity theory. It concludes by suggesting strategies by which adaptive entrepreneurial artist identities can be developed through higher education programs.

Spreading the Word: Garden Writing in the Sub-Tropics [Review]

This article examines local publications regarding horticulture, botany and garden design from the first 50 years of Queensland history.

Quality based frame selection for video face recognition

Quality based frame selection is a crucial task in video face recognition, to both improve the recognition rate and to reduce the computational cost. In this paper we present a framework that uses a variety of cues (face symmetry, sharpness, contrast, closeness of mouth, brightness and openness of the eye) to select the highest quality facial images available in a video sequence for recognition. Normalized feature scores are fused using a neural network and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face recognition system. Experiments on the Honda/UCSD database shows that the proposed method selects the best quality face images in the video sequence, resulting in improved recognition performance.

Tell me what you see : Pre-service teachers' recognition of exemplary digital pedagogy

How do you identify "good" teaching practice in the complexity of a real classroom? How do you know that beginning teachers can recognise effective digital pedagogy when they see it? How can teacher educators see through their students’ eyes? The study in this paper has arisen from our interest in what pre-service teachers “see” when observing effective classroom practice and how this might reveal their own technological, pedagogical and content knowledge. We asked 104 pre-service teachers from Early Years, Primary and Secondary cohorts to watch and comment upon selected exemplary videos of teachers using ICT (information and communication technologies) in Science. The pre-service teachers recorded their observations using a simple PMI (plus, minus, interesting) matrix which were then coded using the SOLO Taxonomy to look for evidence of their familiarity with and judgements of digital pedagogies. From this, we determined that the majority of preservice teachers we surveyed were using a descriptive rather than a reflective strategy, that is, not extending beyond what was demonstrated in the teaching exemplar or differentiating between action and purpose. We also determined that this method warrants wider trialling as a means of evaluating students’ understandings of the complexity of the digital classroom.

QUT Para at TREC 2012 Web Track : Word Associations for Retrieving Web Documents

Many existing information retrieval models do not explicitly take into account in- formation about word associations. Our approach makes use of rst and second order relationships found in natural language, known as syntagmatic and paradigmatic associ- ations, respectively. This is achieved by using a formal model of word meaning within the query expansion process. On ad hoc retrieval, our approach achieves statistically sig- ni cant improvements in MAP (0.158) and P@20 (0.396) over our baseline model. The ERR@20 and nDCG@20 of our system was 0.249 and 0.192 respectively. Our results and discussion suggest that information about both syntagamtic and paradigmatic associa- tions can assist with improving retrieval eectiveness on ad hoc retrieval.

The impact of a model of nurse education to improve knowledge and recognition of delirium in older persons by registered nurses

Introduction: Delirium is a serious issue associated with high morbidity and mortality in older hospitalised people. Early recognition enables diagnosis and treatment of underlying cause/s, which can lead to improved patient outcomes. However, research shows knowledge and accurate nurse recognition of delirium and is poor and lack of education appears to be a key issue related to this problem. Thus, the purpose of this randomised controlled trial (RCT) was to evaluate, in a sample of registered nurses, the usability and effectiveness of a web-based learning site, designed using constructivist learning principles, to improve acute care nurse knowledge and recognition of delirium. Prior to undertaking the RCT preliminary phases involving; validation of vignettes, video-taping five of the validated vignettes, website development and pilot testing were completed. Methods: The cluster RCT involved consenting registered nurse participants (N = 175) from twelve clinical areas within three acute health care facilities in Queensland, Australia. Data were collected through a variety of measures and instruments. Primary outcomes were improved ability of nurses to recognise delirium using written validated vignettes and improved knowledge of delirium using a delirium knowledge questionnaire. The secondary outcomes were aimed at determining nurse satisfaction and usability of the website. Primary outcome measures were taken at baseline (T1), directly after the intervention (T2) and two months later (T3). The secondary outcomes were measured at T2 by participants in the intervention group. Following baseline data collection remaining participants were assigned to either the intervention (n=75) or control (n=72) group. Participants in the intervention group were given access to the learning intervention while the control group continued to work in their clinical area and at that time, did not receive access to the learning intervention. Data from the primary outcome measures were examined in mixed model analyses. Results: Overall, the effect of the online learning intervention over time comparing the intervention group and the control group were positive. The intervention groups‘ scores were higher and the change over time results were statistically significant [T3 and T1 (t=3.78 p=<0.001) and T2 and T1 baseline (t=5.83 p=<0.001)]. Statistically significant improvements were also seen for delirium recognition when comparing T2 and T1 results (t=2.58 p=0.012) between the control and intervention group but not for changes in delirium recognition scores between the two groups from T3 and T1 (t=1.80 p=0.074). The majority of the participants rated the website highly on the visual, functional and content elements. Additionally, nearly 80% of the participants liked the overall website features and there were self-reported improvements in delirium knowledge and recognition by the registered nurses in the intervention group. Discussion: Findings from this study support the concept that online learning is an effective and satisfying method of information delivery. Embedded within a constructivist learning environment the site produced a high level of satisfaction and usability for the registered nurse end-users. Additionally, the results showed that the website significantly improved delirium knowledge & recognition scores and the improvement in delirium knowledge was retained at a two month follow-up. Given the strong effect of the intervention the online delirium intervention should be utilised as a way of providing information to registered nurses. It is envisaged that this knowledge would lead to improved recognition of delirium as well as improvement in patient outcomes however; translation of this knowledge attainment into clinical practice was outside the scope of this study. A critical next step is demonstrating the effect of the intervention in changing clinical behaviour, and improving patient health outcomes.

JFA based Speaker Recognition using Delta-Phase and MFCC features

This paper investigates the use of mel-frequency deltaphase (MFDP) features in comparison to, and in fusion with, traditional mel-frequency cepstral coefficient (MFCC) features within joint factor analysis (JFA) speaker verification. MFCC features, commonly used in speaker recognition systems, are derived purely from the magnitude spectrum, with the phase spectrum completely discarded. In this paper, we investigate if features derived from the phase spectrum can provide additional speaker discriminant information to the traditional MFCC approach in a JFA based speaker verification system. Results are presented which provide a comparison of MFCC-only, MFDPonly and score fusion of the two approaches within a JFA speaker verification approach. Based upon the results presented using the NIST 2008 Speaker Recognition Evaluation (SRE) dataset, we believe that, while MFDP features alone cannot compete with MFCC features, MFDP can provide complementary information that result in improved speaker verification performance when both approaches are combined in score fusion, particularly in the case of shorter utterances.