Long range facial image acquisition and quality

Abstract This chapter introduces issues in long range facial image acquisition and measures for image quality and their usage. Section 1, on image acquisition for face recognition discusses issues in lighting, sensor, lens, blur issues, which impact short-range biometrics, but are more pronounced in long-range biometrics. Section 2 introduces the design of controlled experiments for long range face, and why they are needed. Section 3 introduces some of the weather and atmospheric effects that occur for long-range imaging, with numerous of examples. Section 4 addresses measurements of “system quality”, including image-quality measures and their use in prediction of face recognition algorithm. That section introduces the concept of failure prediction and techniques for analyzing different “quality ” measures. The section ends with a discussion of post-recognition ”failure prediction ” and its potential role as a feedback mechanism in acquisition. Each section includes a collection of open-ended questions to challenge the reader to think about the concepts more deeply. For some of the questions we answer them after they are introduced; others are left as an exercise for the reader. 1 Image Acquisition Before any recognition can even be attempted, they system must acquire an image of the subject with sufficient quality and resolution to detect and recognize the face. The issues examined in this section are the sensor-issues in lighting, image/sensor resolution issues, the field-of view, the depth of field, and effects of motion blur

Real-time Classification of Vehicle Types within Infra-red Imagery

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios

Wide-Scale Analysis of Human Functional Transcription Factor Binding Reveals a Strong Bias towards the Transcription Start Site

We introduce a novel method to screen the promoters of a set of genes with shared biological function, against a precompiled library of motifs, and find those motifs which are statistically over-represented in the gene set. The gene sets were obtained from the functional Gene Ontology (GO) classification; for each set and motif we optimized the sequence similarity score threshold, independently for every location window (measured with respect to the TSS), taking into account the location dependent nucleotide heterogeneity along the promoters of the target genes. We performed a high throughput analysis, searching the promoters (from 200bp downstream to 1000bp upstream the TSS), of more than 8000 human and 23,000 mouse genes, for 134 functional Gene Ontology classes and for 412 known DNA motifs. When combined with binding site and location conservation between human and mouse, the method identifies with high probability functional binding sites that regulate groups of biologically related genes. We found many location-sensitive functional binding events and showed that they clustered close to the TSS. Our method and findings were put to several experimental tests. By allowing a "flexible" threshold and combining our functional class and location specific search method with conservation between human and mouse, we are able to identify reliably functional TF binding sites. This is an essential step towards constructing regulatory networks and elucidating the design principles that govern transcriptional regulation of expression. The promoter region proximal to the TSS appears to be of central importance for regulation of transcription in human and mouse, just as it is in bacteria and yeast.Comment: 31 pages, including Supplementary Information and figure

Robust, Sensitive, and Inexpensive 2D Focal Plane Array Upconverting MMW Imaging Into the Visible

A new concept of millimeter-wave (MMW) imaging system is demonstrated in this letter. The new concept is based on glow discharge detector (GDD) focal plane array (FPA) and a basic CMOS camera. The GDD elements emit optical light as a function of the incident MMW radiation intensity. By synchronizing the modulation of the MMW source with the exposure time of the CMOS camera in a way that every two consecutive frames were captured with the influence of theMMW radiation and without it, we were able to observe the effect of the MMW radiation alone on the GDD FPA without the light deriving from the GDD DC bias

Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery

Object detection from infrared-band (thermal) imagery has been a challenging problem for many years. With the advent of deep Convolutional Neural Networks (CNN), the automated detection and classification of objects of interest within the scene has become popularised due to the notable increases in performance over earlier approaches in the field. These advances in CNN approaches are underpinned by the availability of large-scale, annotated image datasets that are typically available for visible-band (RGB) imagery. By contrast, there is a lack of prior work that specifically targets object detection in infrared-band images, owing to limited datasets availability that stems from more the limited availability and access to infrared-band imagery and associated hardware in general. A viable solution to this problem is transfer learning which can enable the use of such CNN techniques within infrared-band (thermal) imagery, by leveraging prior training on visible-band (RGB) image datasets, and then subsequently only requiring a secondary, smaller volume of infrared-band (thermal) imagery for CNN model fine-tuning. This is performed by adopting an existing pre-trained CNN, pre-optimized for generalized object recognition in visible-band (RGB) imagery, and subsequently fine-tuning the resultant model weights towards our specific infrared-band (thermal) imagery domain task. We use of two state-of-art object detectors, Single Shot Detector (SSD) with a VGG-16 CNN backbone pre-trained on the ImageNet dataset, and You-Only-Look-Once (YOLOV3) with a DarkNet-53 CNN backbone pretrained on the MS-COCO dataset to illustrate our visible-band to infrared band transfer learning paradigm. Exemplar results reported over the FLIR Thermal and MultispectralFIR benchmark datasets show that significant improvements in mAP detection performance to f0.804MsFIR, 0.710FLIRg for SSD and f0.520MsFIR, 0.308FLIRg for YOLOV3 via the use of transfer learning from initial visible-band based CNN training