Combining face detection and novelty to identify important events in a visual lifelog

The SenseCam is a passively capturing wearable camera, worn around the neck and takes an average of almost 2,000 images per day, which equates to over 650,000 images per year. It is used to create a personal lifelog or visual recording of the wearer’s life and generates information which can be helpful as a human memory aid. For such a large amount of visual information to be of any use, it is accepted that it should be structured into “events”, of which there are about 8,000 in a wearer’s average year. In automatically segmenting SenseCam images into events, it is desirable to automatically emphasise more important events and decrease the emphasis on mundane/routine events. This paper introduces the concept of novelty to help determine the importance of events in a lifelog. By combining novelty with face-to-face conversation detection, our system improves on previous approaches. In our experiments we use a large set of lifelog images, a total of 288,479 images collected by 6 users over a time period of one month each

What's on your mind? Recent advances in memory detection using the concealed information test

Lie detectors can be applied in a wide variety of settings. But this advantage comes with a considerable cost: False positives. The applicability of the Concealed Information Test (CIT) is More limited, yet when it can be applied, the risk of false accusations can be set a priori at a very low level. The CIT assesses the recognition of; critical information that is known only by the examiners and the culprit, for example, the face a an accomplice. Large effects are Obtained with the CIT, whether combined with peripheral, brain, or Motor responses. We see three important challenges for the CIT. First, the false negative rate Of the CIT can be substantial, particularly under :realistic circumstantes. A possible solution Seems to restrict the CIT to highly Salient details. Second, there exist effective faking strategies. Future research will tell whether faking can be detected or even prevented (e.g., Using Overt measures). Third, recognition of critical crime detail's does not necessarily result from criminal activity. It is therefore important to properly embed the CIT in the investigative process, While taking care when drawing conclusions from the test outcome (recognition, not guilt)

Outlier Mining Methods Based on Graph Structure Analysis

Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms. While many outlier mining algorithms have been proposed in the literature, they tend to be valid or efficient for specific types of datasets (time series, images, videos, etc.). Here we propose two methods that can be applied to generic datasets, as long as there is a meaningful measure of distance between pairs of elements of the dataset. Both methods start by defining a graph, where the nodes are the elements of the dataset, and the links have associated weights that are the distances between the nodes. Then, the first method assigns an outlier score based on the percolation (i.e., the fragmentation) of the graph. The second method uses the popular IsoMap non-linear dimensionality reduction algorithm, and assigns an outlier score by comparing the geodesic distances with the distances in the reduced space. We test these algorithms on real and synthetic datasets and show that they either outperform, or perform on par with other popular outlier detection methods. A main advantage of the percolation method is that is parameter free and therefore, it does not require any training; on the other hand, the IsoMap method has two integer number parameters, and when they are appropriately selected, the method performs similar to or better than all the other methods tested.Peer ReviewedPostprint (published version

Processing acoustic change and novelty in newborn infants

Research on event-related potential (ERP) correlates of auditory deviance-detection in newborns provided inconsistent results; temporal and topographic ERP characteristics differed widely across studies and individual infants. Robust and reliable ERP responses were, however, obtained to sounds (termed ‘novel’ sounds), which cover a wide range of frequencies and widely differ from the context provided by a repeating sound [Kushnerenko et al., (2002) NeuroReport, 13, 1843–1848]. The question we investigated here is whether this effect can be attributed to novelty per se or to acoustic characteristics of the ‘novel’ sounds, such as their wide frequency spectrum and high signal energy compared with the repeated tones. We also asked how sensitivity to these stimulus aspects changes with development. Twelve newborns and 11 adults were tested in four different oddball conditions, each including a ‘standard’ sound presented with the probability of 0.8 and two types of infrequent ‘deviant’ sounds (0.1 probability, each). Deviants were (i) ‘novel’ sounds (diverse environmental noises); (ii) white-noise segments, or harmonic tones of (iii) a higher pitch, or (iv) higher intensity. In newborns, white-noise deviants elicited the largest response in all latency ranges, whereas in adults, this phenomenon was not found. Thus, newborns appear to be especially sensitive to sounds having a wide frequency spectrum. On the other hand, the pattern of results found for the late discriminative ERP response indicates that newborns may also be able to detect novelty in acoustic stimulation, although with a longer latency than adults, as shown by the ERP response. Results are discussed in terms of developmental refinement of the initially broadly tuned neonate auditory system