A person's iris pattern is encoded into an Iris Code, which is stored in a database and used for recognition when a live iris is presented for comparison.
In third stage, an adaptive network based fuzzy inference system (ANFIS) classifier is used for recognition process.
The neural network is to extract essential information from the signals contaminated with noise and has been used for recognition and classification.
This suggests that different neural systems may, to some extent, be used for recognition of sheep as opposed to human faces.
For this, HMMs for the different types of word-stress unit contours were trained and then used for recognition and alignment of such units from the input speech.
Although the aspects could be used for recognition from one image, the full information stored in the AG (aspects and visual events) suggests a multiple views, possibly active, approach.
In this paper we propose (based on testing results) a Neural Network structures that can be used for recognition of presence and absence of internal organ on images received from endoscope.
The {dAb AuNR FCA} labels were designed by exploiting silica-functionalized gold nanorods (AuNR@SiO2) as the carriers for immobilization of detection antibody (dAb) and ferrocenecarboxylic acid (FCA), in which dAb was used for recognition of E. coli and FCA tags served as signal-generating molecule.
The weighted finite state machine decoder was used for recognition.
The database can be used for recognition of handwritten Tamil words.
Interestingly, the optimal decoding strategy is apparently dependent on the features that are used for recognition.
