Automatic Recognition of Facial Features and Landmarking of Digital Human Head

Ergonomic design of products demands accurate human dimensions-anthropometric data. Manual measurement over live subjects, has several limitations like long time, required presence of subjects for every new measurement, physical contact etc. Hence the data currently available is limited and anthropometric data related to facial features is difficult to obtain. In this paper, we discuss a methodology to automatically detect facial features and landmarks from scanned human head models. Segmentation of face into meaningful patches corresponding to facial features is achieved by Watershed algorithms and Mathematical Morphology tools. Many Important physiognomical landmarks are identified heuristically.

Simulation-driven design of helmets for head impact protection using an injury-based criterion

The current paper suggests a new procedure for designing helmets for head impact protection for users such as motorcycle riders. According to the approach followed here, a helmet is mounted on a featureless Hybrid 3 headform that is used in assessing vehicles for compliance to the FMVSS 201 regulation in the USA for upper interior head impact safety. The requirement adopted in the latter standard, i.e. not exceeding a threshold HIC(d) limit of 1000, is applied in the present study as a likely criterion for adjudging the efficacy of helmets. An impact velocity of 6 m/s (13.5 mph) for the helmet-headform system striking a rigid target can probably be acceptable for ascertaining a helmet's effectiveness as a countermeasure for minimizing the risk of severe head injury. The proposed procedure is demonstrated with the help of a validated LS-DYNA model of a featureless Hybrid 3 headform in conjunction with a helmet model comprising an outer polypropylene shell to the inner surface of which is bonded a protective polyurethane foam padding of a given thickness. Based on simulation results of impact on a rigid surface, it appears that a minimum foam padding thickness of 40 mm is necessary for obtaining an acceptable value of HIC(d).

Turning visual search time on its head

Our everyday visual experience frequently involves searching for objects in clutter. Why are some searches easy and others hard? It is generally believed that the time taken to find a target increases as it becomes similar to its surrounding distractors. Here, I show that while this is qualitatively true, the exact relationship is in fact not linear. In a simple search experiment, when subjects searched for a bar differing in orientation from its distractors, search time was inversely proportional to the angular difference in orientation. Thus, rather than taking search reaction time (RT) to be a measure of target-distractor similarity, we can literally turn search time on its head (i.e. take its reciprocal 1/RT) to obtain a measure of search dissimilarity that varies linearly over a large range of target-distractor differences. I show that this dissimilarity measure has the properties of a distance metric, and report two interesting insights come from this measure: First, for a large number of searches, search asymmetries are relatively rare and when they do occur, differ by a fixed distance. Second, search distances can be used to elucidate object representations that underlie search - for example, these representations are roughly invariant to three-dimensional view. Finally, search distance has a straightforward interpretation in the context of accumulator models of search, where it is proportional to the discriminative signal that is integrated to produce a response. This is consistent with recent studies that have linked this distance to neuronal discriminability in visual cortex. Thus, while search time remains the more direct measure of visual search, its reciprocal also has the potential for interesting and novel insights. (C) 2012 Elsevier Ltd. All rights reserved.

Influence of the Blade Hub Geometry on the Performance of Low-Head Axial Flow Turbines

The influence of geometric parameters, such as blade profile and hub geometry on axial flow turbines for micro hydro application remains poorly characterized. This paper first introduces a holistic theoretical model for studying the hydraulic phenomenon resulting from geometric modification to the blades. It then describes modification carried out on two runner stages, of which one has untwisted blades and the other has twisted blades obtained by modifying the inlet hub. The experimental results showed that the performance of the untwisted blade runner was satisfactory with a maximum efficiency of 68%. However, positive effects of twisted blades were clearly evident with an efficiency rise of more than 2%. This study also looks into the possible limitations of the model and suggests the extension of the experimental work and the use of computational tools to conduct a progressive validation of all experimental findings, especially on the flow physics within the hub region and the slip phenomena. The paper finally underlines the importance of developing a standardization philosophy for axial flow turbines specific for micro hydro requirements. DOI:10.1061/(ASCE)EY.1943-7897.0000060. (C) 2012 American Society of Civil Engineers.

Boosting-based transfer learning for multi-view head-pose classification from surveillance videos

This work proposes a boosting-based transfer learning approach for head-pose classification from multiple, low-resolution views. Head-pose classification performance is adversely affected when the source (training) and target (test) data arise from different distributions (due to change in face appearance, lighting, etc). Under such conditions, we employ Xferboost, a Logitboost-based transfer learning framework that integrates knowledge from a few labeled target samples with the source model to effectively minimize misclassifications on the target data. Experiments confirm that the Xferboost framework can improve classification performance by up to 6%, when knowledge is transferred between the CLEAR and FBK four-view headpose datasets.

An adaptation framework for head-pose classification in dynamic multi-view scenarios

Multi-view head-pose estimation in low-resolution, dynamic scenes is difficult due to blurred facial appearance and perspective changes as targets move around freely in the environment. Under these conditions, acquiring sufficient training examples to learn the dynamic relationship between position, face appearance and head-pose can be very expensive. Instead, a transfer learning approach is proposed in this work. Upon learning a weighted-distance function from many examples where the target position is fixed, we adapt these weights to the scenario where target positions are varying. The adaptation framework incorporates reliability of the different face regions for pose estimation under positional variation, by transforming the target appearance to a canonical appearance corresponding to a reference scene location. Experimental results confirm effectiveness of the proposed approach, which outperforms state-of-the-art by 9.5% under relevant conditions. To aid further research on this topic, we also make DPOSE- a dynamic, multi-view head-pose dataset with ground-truth publicly available with this paper.

Exploring Transfer Learning Approaches for Head Pose Classification from Multi-view Surveillance Images

Head pose classification from surveillance images acquired with distant, large field-of-view cameras is difficult as faces are captured at low-resolution and have a blurred appearance. Domain adaptation approaches are useful for transferring knowledge from the training (source) to the test (target) data when they have different attributes, minimizing target data labeling efforts in the process. This paper examines the use of transfer learning for efficient multi-view head pose classification with minimal target training data under three challenging situations: (i) where the range of head poses in the source and target images is different, (ii) where source images capture a stationary person while target images capture a moving person whose facial appearance varies under motion due to changing perspective, scale and (iii) a combination of (i) and (ii). On the whole, the presented methods represent novel transfer learning solutions employed in the context of multi-view head pose classification. We demonstrate that the proposed solutions considerably outperform the state-of-the-art through extensive experimental validation. Finally, the DPOSE dataset compiled for benchmarking head pose classification performance with moving persons, and to aid behavioral understanding applications is presented in this work.

Inhibition of p300 lysine acetyltransferase activity by luteolin reduces tumor growth in head and neck squamous cell carcinoma (HNSCC) xenograft mouse model

Chromatin acetylation is attributed with distinct functional relevance with respect to gene expression in normal and diseased conditions thereby leading to a topical interest in the concept of epigenetic modulators and therapy. We report here the identification and characterization of the acetylation inhibitory potential of an important dietary flavonoid, luteolin. Luteolin was found to inhibit p300 acetyltransferase with competitive binding to the acetyl CoA binding site. Luteolin treatment in a xenografted tumor model of head and neck squamous cell carcinoma (HNSCC), led to a dramatic reduction in tumor growth within 4 weeks corresponding to a decrease in histone acetylation. Cells treated with luteolin exhibit cell cycle arrest and decreased cell migration. Luteolin treatment led to an alteration in gene expression and miRNA profile including up-regulation of p53 induced miR-195/215, let7C; potentially translating into a tumor suppressor function. It also led to down regulation of oncomiRNAs such as miR-135a, thereby reflecting global changes in the microRNA network. Furthermore, a direct correlation between the inhibition of histone acetylation and gene expression was established using chromatin immunoprecipitation on promoters of differentially expressed genes. A network of dysregulated genes and miRNAs was mapped along with the gene ontology categories, and the effects of luteolin were observed to be potentially at multiple levels: at the level of gene expression, miRNA expression and miRNA processing.