Deep Feature-based Face Detection on Mobile Devices

We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front facing camera. The proposed method is able to detect faces in images containing extreme pose and illumination variations as well as partial faces. The main challenge in developing deep feature-based algorithms for mobile devices is the constrained nature of the mobile platform and the non-availability of CUDA enabled GPUs on such devices. Our implementation takes into account the special nature of the images captured by the front-facing camera of mobile devices and exploits the GPUs present in mobile devices without CUDA-based frameorks, to meet these challenges.Comment: ISBA 201

Face Detection and Recognition using Skin Segmentation and Elastic Bunch Graph Matching

Recently, face detection and recognition is attracting a lot of interest in areas such as network security, content indexing and retrieval, and video compression, because ‘people’ are the object of attention in a lot of video or images. To perform such real-time detection and recognition, novel algorithms are needed, which better current efficiencies and speeds. This project is aimed at developing an efficient algorithm for face detection and recognition. This project is divided into two parts, the detection of a face from a complex environment and the subsequent recognition by comparison. For the detection portion, we present an algorithm based on skin segmentation, morphological operators and template matching. The skin segmentation isolates the face-like regions in a complex image and the following operations of morphology and template matching help reject false matches and extract faces from regions containing multiple faces. For the recognition of the face, we have chosen to use the ‘EGBM’ (Elastic Bunch Graph Matching) algorithm. For identifying faces, this system uses single images out of a database having one image per person. The task is complex because of variation in terms of position, size, expression, and pose. The system decreases this variance by extracting face descriptions in the form of image graphs. In this, the node points (chosen as eyes, nose, lips and chin) are described by sets of wavelet components (called ‘jets’). Image graph extraction is based on an approach called the ‘bunch graph’, which is constructed from a set of sample image graphs. Recognition is based on a directly comparing these graphs. The advantage of this method is in its tolerance to lighting conditions and requirement of less number of images per person in the database for comparison

Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

Partial Face Detection and Illumination Estimation

Face Analysis has long been a crucial component of many security applications. In this work, we shall propose and explore some face analysis algorithms which are applicable to two different security problems, namely Active Authentication and Image Tampering Detection. In the first section, we propose two algorithms, “Deep Feature based Face Detection for Mobile Devices” and “DeepSegFace” that are useful in detecting partial faces such as those seem in typical Active Authentication scenarios. In the second section, we propose an algorithm to detect discrepancies in illumination conditions given two face images, and use that as an indication to decide if an image has been tampered by transplanting faces. We also extend the illumination detection algorithm by proposing an adversarial data augmentation scheme. We show the efficacy of the proposed algorithms by evaluating them on multiple datasets

A quantitative assessment of distributions and sources of tropospheric halocarbons measured in Singapore.

This work reports the first ground-based atmospheric measurements of 26 halocarbons in Singapore, an urban-industrial city-state in Southeast (SE) Asia. A total of 166 whole air canister samples collected during two intensive 7 Southeast Asian Studies (7SEAS) campaigns (August-October 2011 and 2012) were analyzed for C1-C2 halocarbons using gas chromatography-electron capture/mass spectrometric detection. The halocarbon dataset was supplemented with measurements of selected non-methane hydrocarbons (NMHCs), C1-C5 alkyl nitrates, sulfur gases and carbon monoxide to better understand sources and atmospheric processes. The median observed atmospheric mixing ratios of CFCs, halons, CCl4 and CH3CCl3 were close to global tropospheric background levels, with enhancements in the 1-17% range. This provided the first measurement evidence from SE Asia of the effectiveness of Montreal Protocol and related national-scale regulations instituted in the 1990s to phase-out ozone depleting substances (ODS). First- and second-generation CFC replacements (HCFCs and HFCs) dominated the atmospheric halocarbon burden with HFC-134a, HCFC-22 and HCFC-141b exhibiting enhancements of 39-67%. By combining near-source measurements in Indonesia with receptor data in Singapore, regionally transported peat-forest burning smoke was found to impact levels of several NMHCs (ethane, ethyne, benzene, and propane) and short-lived halocarbons (CH3I, CH3Cl, and CH3Br) in a subset of the receptor samples. The strong signatures of these species near peat-forest fires were potentially affected by atmospheric dilution/mixing during transport and by mixing with substantial urban/regional backgrounds at the receptor. Quantitative source apportionment was carried out using positive matrix factorization (PMF), which identified industrial emissions related to refrigeration, foam blowing, and solvent use in chemical, pharmaceutical and electronics industries as the major source of halocarbons (34%) in Singapore. This was followed by marine and terrestrial biogenic activity (28%), residual levels of ODS from pre-Montreal Protocol operations (16%), seasonal incidences of peat-forest smoke (13%), and fumigation related to quarantine and pre-shipment (QPS) applications (7%)

Magnitude and Correlates of Elevated Blood Pressure among Adolescent School Students Aged 15-19 Years in a Block of Murshidabad, West Bengal, India

Background: The prevalence of adolescent hypertension is on the rise due to multiplicity of certain risk factors, like obesity, unhealthy dietary behaviour, physical inactivity, tobacco use, alcohol addiction, and academic stress. The present study aimed to estimate the prevalence of elevated blood pressure and hypertension among adolescent school children and identify the factors influencing it.Methods: The present observational, cross-sectional study was conducted in two higher secondary schools in a block of Murshidabad district, West Bengal, from February to April 2021. The subjects included 15 to 19-year-old school students. Multistage random sampling method was used for selecting a sample size of 183 adolescent school children. Data were obtained by interviewing the study participants, measurement of blood pressure and anthropometric measurements. Chi-squared test and binary logistic regression were used for bivariate and Multivariable data analysis, respectively, with P<0.05 as the level of significance.Results: The mean of Systolic Blood Pressure and Diastolic Blood Pressure were 115.02+10.853 and 71.52+8.484 mm of Hg, respectively. The overall prevalence of elevated blood pressure and adolescent hypertension was 21.3% (95% CI 15.4-27.2). The prevalence was significantly higher among those with paternal education of above middle school (AOR=1.803, P=0.011), high socioeconomic status (AOR=3.16, P=0.02), and high Body Mass Index for their age (AOR=11.474, P<0.0001). Smart phone use (P=0.03) and family history of hypertension (P=0.029) were also found to significantly influence elevated blood pressure among the subjects in bivariate analysis.Conclusions: Measurement of blood pressure, as a part of school health programme, should be given priority with emphasis on physical activity at school, health promotion to avoid unhealthy diet, and restricted smart phone use