Most of matching or verification phases of fingerprint systems use minutiae types and orientation angle to find matched minutiae pairs from the input and template fingerprints. Unfortunately, due to some non-linear distortions, like excessive pressure and fingers twisting during enrollment, this process can cause the minutiae features to be distorted from the original. The authors are then interested in a fingerprint matching method using contactless images for fingerprint verification. After features extraction, they compute Euclidean distances between template minutiae (bifurcation and ending points) and input image minutiae. They compute then after bifurcation ridges orientation angles and ending point orientations. In the decision stage, they analyze the similarity between templates. The proposed algorithm has been tested on a set of 420 fingerprint images. The verification accuracy is found to be acceptable and the experimental results are promising.
The GRAFCET standard (IEC 60848) is one of the convenient formalisms used to specify the behaviour of the automated systems. Being just a semi-formal language, the usual practice is to go through an unambiguous formalism such as time Petri net (TPN) in order to validate a specification expressed by a GRAFCET model. In this paper, we propose how to perform model-checking on a GRAFCET model translated into a ε-TPN, specifically with State-Event Linear Temporal Logic (SE-LTL). Especially, we provide a way to take into account quantitative time constraints verification by integrating observers in the ε-TPN intermediate model, since TPN state-space abstractions do not allow directly such kind of model-checking.
Emotion recognition is an important aspect of affective computing, one of whose aims is the study and development of behavioral and emotional interaction between human and machine. In this context, another important point concerns acquisition devices and signal processing tools which lead to an estimation of the emotional state of the user. This article presents a survey about concepts around emotion, multimodality in recognition, physiological activities and emotional induction, methods and tools for acquisition and signal processing with a focus on processing algorithm and their degree of reliability.
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