Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face recognition. To our knowledge, this is also the only such study to incorporate signicant time lapse between gallery and probe image acquisition, and to look at the effect of depth resolution. Recognition results are obtained in (1) single gallery and a single probe study, and (2) a single gallery and multiple probe study. A total of 275 subjects participated in one or more data acquisition sessions. Results are presented for gallery and probe datasets of 200 subjects imaged in both 2D and 3D, with one to thirteen weeks time lapse between gallery and probe images of a given subject yielding 951 pairs of 2D and 3D images. Using a PCA-based approach tuned separately for 2D and for 3D, we nd that 3D outperforms 2D. However, we also nd a multi-modal rank-one recognition rate of 98.5% in a single probe study and 98.8% in multi-probe study, which is statistically signicantly greater than either 2D or 3D alone.

Face Recognition Using 2D and 3D Facial Data

Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face data for biometric recognition. To our knowledge, this is also the only such study to incorporate significant time lapse between gallery and probe image acquisition. Recognition results are presented for gallery and probe datasets of 166 subjects imaged in both 2D and 3D, with six to thirteen weeks time lapse between gallery and probe images of a given subject. Using a PCA-based approach tuned separately for 2D and for 3D, we find no statistically significant difference between the rank-one recognition rates of 83.1% for 2D and 83.7% for 3D. Using a certainty-weighted sum-of-distance approach to combining 2D and 3D, we find a multimodal rank-one recognition rate of 92.8%, which is statistically significantly greater than either 2D or 3D alone.

Multimodal 2D and 3D biometrics for face recognition

In Shannon's classical model of transmitting a message over a noisy channel we have the following situation:

There are two persons called sender and receiver. Sender and receiver can communicate via a channel. In the simplest case the sender just puts some input letters into the channel and the receiver gets some output letters. Usually the channel is noisy, i.e. the channel output is a random variable whose distribution is governed by the input letters. This model can be extended in several ways: Channels with passive feedback for example give the output letters back to the sender. Multiuser channels like multiple access channels or broadcast channels (which will not be considered in this paper) have several senders or receivers which want to communicate simultaneously. Common to all these models of transmission is the task that sender and receiver have to perform: Both have a common message setM and the sender is given a messagei∈M. He has to encode the message (i.e. transform it into a sequence of input letters for the channel) in such a way, that the receiver can decode the sequence of output letters so that he can decide with a small probability of error what the message i was. The procedures for encoding and decoding are called a code for the channel and the number of times the channel is used to transmit one message is called the blocklength of the code.

On identification

https://marathon.csee.usf.edu/~sarkar/PDFs/IVC-GaitFace.pdf

Outdoor recognition at a distance by fusing gait and face

Classification of major construction materials in construction environments using ensemble classifiers

In this paper we present a system for person identification which is based on the combination of several face classifiers. First, we describe briefly the individual face classifiers. The classifier combination is then discussed. Finally, the impact of the combination is presented in a series of practical experiments which show a significant improvement of the recognition rate.

Combination of face classifiers for person identification

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Combination of face classifiers for person identification