The effect of image alterations on identification using palmar flexion creases.

Palmprints are identified using matching of minutia points, which can be time consuming for fingerprint experts and in database searches. This article analyzes the operational characteristics of a palmar flexion crease (PFC) identification software tool, using a dataset of 10 replicates of 100 palms, where the user can label and match palmar line features. Results show that 100 palmprint images modified 10 times each using rotation, translation, and additive noise, mimicking some of the characteristics found in crime scene palmar marks, can be identified with a 99.2% genuine acceptance rate and 0% false acceptance rate when labeled within 3.5 mm of the PFC. Partial palmprint images can also be identified using the same method to filter the dataset prior to traditional matching, while maintaining an effective genuine acceptance rate. The work shows that identification using PFCs can improve palmprint identification through integration with existing systems, and through dedicated palmprint identification applications.

Preliminary analysis of the nature and processing of palm marks by a U.K. fingerprint bureau.

The nature of crime scene palmar images (CSPI) or factors affecting search parameters using a palm-enabled AFIS system have not been investigated. A questionnaire-based survey, undertaken by U.K. fingerprint experts utilizing the U.K.'s IDENT-1 system during the period January to July 2010, of CSPI marks has been conducted to provide descriptive statistical data on the nature of CSPI and some aspects of the ACE-V process. 45 scene-recovered marks were analyzed for part of the CSPI recovered, friction ridge detail, and process times. U.K. population handedness was different from recovered CSPI. Most and least frequently recovered regions were hypothenar pad B and the central pad, respectively. There was a nonsignificant association between palm region and number of palm regions recovered, as well as identification rate and analysis times and characteristics. The number of CSPI regions was significantly related to time for analysis, identification, and comparison.

Collective emotions online and their influence on community life.

BACKGROUND: E-communities, social groups interacting online, have recently become an object of interdisciplinary research. As with face-to-face meetings, Internet exchanges may not only include factual information but also emotional information--how participants feel about the subject discussed or other group members. Emotions in turn are known to be important in affecting interaction partners in offline communication in many ways. Could emotions in Internet exchanges affect others and systematically influence quantitative and qualitative aspects of the trajectory of e-communities? The development of automatic sentiment analysis has made large scale emotion detection and analysis possible using text messages collected from the web. However, it is not clear if emotions in e-communities primarily derive from individual group members' personalities or if they result from intra-group interactions, and whether they influence group activities. METHODOLOGY/PRINCIPAL FINDINGS: Here, for the first time, we show the collective character of affective phenomena on a large scale as observed in four million posts downloaded from Blogs, Digg and BBC forums. To test whether the emotions of a community member may influence the emotions of others, posts were grouped into clusters of messages with similar emotional valences. The frequency of long clusters was much higher than it would be if emotions occurred at random. Distributions for cluster lengths can be explained by preferential processes because conditional probabilities for consecutive messages grow as a power law with cluster length. For BBC forum threads, average discussion lengths were higher for larger values of absolute average emotional valence in the first ten comments and the average amount of emotion in messages fell during discussions. CONCLUSIONS/SIGNIFICANCE: Overall, our results prove that collective emotional states can be created and modulated via Internet communication and that emotional expressiveness is the fuel that sustains some e-communities.