Evaluation of three remote camera systems for detecting mammals and birds

Automated camera systems have widespread application in wildlife studies and their use is increasing (Kucera & Barrett 1993; Cutler & Swann 1999; Swann et al. 2004; Parker et al. 2008). Among other applications, they have been used to produce species inventories, estimate population sizes, study behaviour and examine the impact and activity of predators (Cutler & Swann 1999; Swann et al. 2004). Modern camera systems can operate for extended durations, are relatively non-invasive, easy to operate, portable, durable and can take good-quality images by day and night (Kucera & Barrett 1993; Peterson & Thomas 1998; Allison & Destefano 2006; Parker et al. 2008). Beyond their scientific applications, the generation of high-quality images can be useful for educational and conservation purposes (Cutler & Swann 1999). The two most common types of systems currently used in ecological research are passive and active infrared (IR) systems (Cutler & Swann 1999; Parker et al. 2008). An older form of remote photography is video which captures a continuous record of activity at a focal site (Stewart et al.1997; King et al. 2001). Camera systems have certain limitations and biases (Swann et al. 2004), yet these have not been well studied. Refinement of the use of camera systems is required to fully realize their value (Towerton et al. 2008). Here, we describe a comparison of detection rates of mammals and birds by passive and active IR camera systems, using a video system to benchmark detection rates.

Fourier transform infrared microspectroscopy as a tool to identify macroalgal propagules

Understanding of macroalgal dispersal has been hindered by the difficulty in identifying propagules. Different carrageenans typically occur in gametophytes and tetrasporophytes of the red algal family Gigartinaceae, and we may expect that carpospores and tetraspores also differ in composition of carrageenans. Using Fourier transform infrared (FT-IR) microspectroscopy, we tested the model that differences in carrageenans and other cellular constituents between nuclear phases should allow us to discriminate carpospores and tetraspores of Chondrus verrucosus Mikami. Spectral data suggest that carposporophytes isolated from the pericarp and female gametophytes contained κ-carrageenan, whereas tetrasporophytes contained λ-carrageenan. However, both carpospores and tetraspores exhibited absorbances in wave bands characteristic of κ-,ι-, and λ-carrageenans. Carpospores contained more proteins and may be more photosynthetically active than tetraspores, which contained more lipid reserves. We draw analogies to planktotrophic and lecithotrophic larvae. These differences in cellular chemistry allowed reliable discrimination of spores, but pretreatment of spectral data affected the accuracy of classification. The best classification of spores was achieved with extended multiplicative signal correction (EMSC) pretreatment using partial least squares discrimination analysis, with correct classification of 86% of carpospores and 83% of tetraspores. Classification may be further improved by using synchrotron FT-IR microspectroscopy because of its inherently higher signal-to-noise ratio compared with microspectroscopy using conventional sources of IR. This study demonstrates that FT-IR microspectroscopy and bioinformatics are useful tools to advance our understanding of algal dispersal ecology through discrimination of morphologically similar propagules both within and potentially between species.

Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in practice. While inherently insensitive to visible spectrum illumination changes, IR data introduces specific challenges of its own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, when comparing two thermal IR images of faces, we mutually normalize their poses and facial expressions by using an active appearance model (AAM) to generate synthetic images of the two faces with a neutral facial expression and in the same view (the average of the two input views). This is achieved by piecewise affine warping which follows AAM fitting. A major contribution of our work is the use of an AAM ensemble in which each AAM is specialized to a particular range of poses and a particular region of the thermal IR face space. Combined with the contributions from our previous work which addressed the problem of reliable AAM fitting in the thermal IR spectrum, and the development of a person-specific representation robust to transient changes in the pattern of facial temperature emissions, the proposed ensemble framework accurately matches faces across the full range of yaw from frontal to profile, even in the presence of scale variation (e.g. due to the varying distance of a subject from the camera). The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces and a newly acquired data set of thermal IR motion videos. Our approach achieved perfect recognition performance on both data sets, significantly outperforming the current state of the art methods even when they are trained with multiple images spanning a range of head views.

Infrared face recognition: a literature review

Automatic face recognition (AFR) is an area with immense practical potential which includes a wide range of commercial and law enforcement applications, and it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in AFR continues to improve, benefiting from advances in a range of different fields including image processing, pattern recognition, computer graphics and physiology. However, systems based on visible spectrum images continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease their accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject.

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition; (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies; (iii) a description of the main databases of infrared facial images available to the researcher; and lastly (iv) a discussion of the most promising avenues for future research. © 2014 Elsevier Ltd.