automatic poor quality rejecter machine




Automatic detection of poor speech recognition at the dialogue level
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 of the 37th annual meeting of the , 1999 ,dl.acm.org
 use the machine learning program RIPPER (Co- hen, 1996) to automatically induce a “poor
speech  First, each utterance that was not rejected by automatic speech recognition (ASR) was
manually  The dialogue quality features attempt to capture aspects of the naturalness of the 

FACE RECOGNITION IN POOR-QUALITY VIDEO: Evidence From Security Surveillance
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AM Burton, S Wilson, M Cowan ,Citeseer
Abstract—Security surveillance systems often produce poor-quality video, and this may be
problematic in gathering forensic evidence. We examined the ability of subjects to identify
target people captured by a commercially available video security device. In Experiment 1,
all stimuli, subjects correctly identified 73% of the familiar targets, and correctly rejected 92%
of  In the particular case of poor-quality video and targets unlikely to be familiar to viewers  However,
in the case of unfamiliar-face recognition, it is clear that automatic procedures that

Multibiometric systems
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 Communications of the ACM, 2004 ,dl.acm.org
 Biometrics refers to the automatic identification (or verification) of an individual (or a claimed
identity) by using certain physiological or  four impressions of a user’s fingerprint shown here cannot
be enrolled by most fingerprint systems, due to the poor image quality of the 

Classification of fingerprint images
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L Hong ,Proceedings of the Scandinavian Conference on , 1999 ,cse.msu.edu
 rates are listed in Table 2. 5. Summary and Conclusions Fingerprint classification provides an
important in- dexing mechanism for automatic fingerprint identifica  configuration and poor quality
of input images, the de- sired accuracy of 1 % error rate at 20% reject rate is 

Data quality in context
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DM Strong, YW Lee ,Communications of the ACM, 1997 ,dl.acm.org
 data would not solve their analyzability prob- lems; thus, they partially automated their patient 
The results confirm the importance of the quality categories and dimensions in our previous  argue
our research findings can be attributed to poor management or poor IS organiza 

Fingerprint classification
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K Karu ,Pattern recognition, 1996
 For an automatic system, the problem is much more difficult because the system must take into
account the global directions of the ridges as  Images of poor quality, such as those in Fig.  It would
be desirable if our algorithm could reject these images or classify them as “unknown 

Filterbank-based fingerprint matching
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  S Prabhakar, L Hong ,Image Processing, IEEE , 2000
 eg, minutiae-based fingerprint matching systems [1], [5]) or exclusively global information
(fingerprint classification based on the Henry system [6]–[8]). The minutiae-based automatic
identification techniques  Fig. 12. Examples of rejected images: (a) a poor quality image and (b 


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