Crime Detection and Criminal Recognition to Intervene in Interpersonal Violence Using Deep Convolutional Neural Network With Transfer Learning

Interpersonal violence, such as physical and sexual abuse, eve-teasing, bullying, and taking hostages, is a growing concern in our society. The criminals who directly or indirectly committed the crime often do not go into the trial for the lack of proper evidence as it is very tough to collect photographic proof of the incident. A subject's corneal reflection has the potentiality to reveal the bystander images. Motivated with this clue, a novel approach is proposed in the current paper that uses a convolutional neural network (CNN) along with transfer learning in identifying crime as well as recognizing the criminals from the corneal reflected image of the victim called the Purkinje image. This study found that off-the-shelf CNN can be fine-tuned to extract discriminative features from very low resolution and noisy images. The procedure is validated using the developed datasets comprising six different subjects taken at diverse situations. They confirmed that it has the ability to recognize criminals from corneal reflection images with an accuracy of 95.41%.

Small eye movements cannot be reliably measured by video-based P-CR eye-trackers

For evaluating whether an eye-tracker is suitable for measuring microsaccades, Poletti & Rucci (2016) propose that a measure called ‘resolution’ could be better than the more established root-mean-square of the sample-to-sample distances (RMS-S2S). Many open questions exist around the resolution measure, however. Resolution needs to be calculated using data from an artificial eye that can be turned in very small steps. Furthermore, resolution has an unclear and uninvestigated relationship to the RMS-S2S and STD (standard deviation) measures of precision (Holmqvist & Andersson, 2017, p. 159-190), and there is another metric by the same name (Clarke, Ditterich, Drüen, Schönfeld, and Steineke 2002), which instead quantifies the errors of amplitude measurements. In this paper, we present a mechanism, the Stepperbox, for rotating artificial eyes in arbitrary angles from 1′ (arcmin) and upward. We then use the Stepperbox to find the minimum reliably detectable rotations in 11 video-based eye-trackers (VOGs) and the Dual Purkinje Imaging (DPI) tracker. We find that resolution correlates significantly with RMS-S2S and, to a lesser extent, with STD. In addition, we find that although most eye-trackers can detect some small rotations of an artificial eye, the rotations of amplitudes up to 2∘ are frequently erroneously measured by video-based eye-trackers. We show evidence that the corneal reflection (CR) feature of these eye-trackers is a major cause of erroneous measurements of small rotations of artificial eyes. Our data strengthen the existing body of evidence that video-based eye-trackers produce errors that may require that we reconsider some results from research on reading, microsaccades, and vergence, where the amplitude of small eye movements have been measured with past or current video-based eye-trackers. In contrast, the DPI reports correct rotation amplitudes down to 1′.

“Pervasive ocular tremor of Parkinson’s” is not pervasive, ocular, or uniquely parkinsonian

A putative tremulous oscillation of the eyes has been proposed to provide the long-sought objective, sensitive and specific diagnostic sign for Parkinson’s disease . Vigorous debate has ensued as to whether this “ocular tremor” is a real phenomenon or an artefact secondary to head motion. Contradictory arguments have centred on the capabilities of magnetic movement measurement technologies, with small numbers of illustrative cases presented to bolster either side. A large, definitive study able to either confirm or reject the original finding on a purely oculographic basis was required. We examined 681 oculomotor recordings from a longitudinal study of 188 Parkinson’s patients and 66 controls. Fourier analysis was used to detect oscilla- tions not only in their raw pupil motion data but also in corneal reflection signals, which can correct for head motion. We replicated the original authors’ findings inasmuch as we detected oscillations in raw pupil position data that often occurred in their reported frequency range of 5.7 ± 3 Hz. They were not, however, present universally in patients and the frequency (if not power) distribution was similar in controls. Crucially, oscillations in that frequency range were abolished when corneal reflection correction was applied to compensate for head-motion. The oscillations in the uncorrected data were strongly related to clinical ratings of somatomotor tremor severity. We found strong evidence that the postulated purely ocular tremor of Parkin- son’s does not exist, other than as an artefact arising from head motion secondary to somatic tremor.

Motion tracking of iris features to detect small eye movements

The inability of current video-based eye trackers to reliably detect very small eye movements has led to confusion about the prevalence or even the existence of monocular microsaccades (small, rapid eye movements that occur in only one eye at a time). As current methods often rely on precisely localizing the pupil and/or corneal reflection on successive frames, current microsaccade-detection algorithms often suffer from signal artifacts and a low signal-to-noise ratio. We describe a new video-based eye tracking methodology which can reliably detect small eye movements over 0.2 degrees (12 arcmin) with very high confidence. Our method tracks the motion of iris features to estimate velocity rather than position, yielding a better record of microsaccades. We provide a more robust, detailed record of miniature eye movements by relying on more stable, higher-order features (such as local features of iris texture) instead of lower-order features (such as pupil center and corneal reflection), which are sensitive to noise and drift.