Abstract

Many security and surveillance tasks involve either finding an object in a cluttered scene or discriminating between like objects. For example, an observer might look for a person of known height and weight in a crowd, or he might want to positively identify a specific face. The paper “Modeling target acquisition tasks associated with security and surveillance” [Appl. Opt. 46, 4209 (2007)] describes a specific-object model used to predict the probability of accomplishing this type of task. We describe four facial identification experiments and apply the specific-object model to predict the results. Facial identification is accurately predicted by the specific-object model.

© 2008 Optical Society of America

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References

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  1. R. Vollmerhausen and A. L. Robinson, “Modeling target acquisition tasks associated with security and surveillance,” Appl. Opt. 46, 4209-4221 (2007).
    [CrossRef] [PubMed]
  2. R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43, 2806-2818 (2004).
    [CrossRef]
  3. R. H. Vollmerhausen, E. Jacobs, J. Hixson, and M. Friedman, “The targeting task performance (TTP) metric; a new model for predicting target acquisition performance,” Tech Report AMSEL-NV-TR-230, NVESD, US Army CERDEC, Fort Belvoir, Va., March 2005.
  4. P. G. J. Barten, “Formula for the contrast sensitivity of the human eye,” Proc. SPIE 5294, 231-238 (2004). Paper available on the Web at http://www.SPIE.org.
    [CrossRef]
  5. R. H. Vollmerhausen, “Incorporating display limitations into night vision performance models,” IRIS Passive Sensors 2, 11-31 (1995).
  6. R. H. Vollmerhausen, “Modeling the performance of imaging sensors,” in Electro-Optical Imaging: System Performance and Modeling, Chapter 12, L. Biberman, ed. (SPIE Press, Bellingham, Wash., 2000).
  7. R. G. Driggers, R. Vollmerhausen, and K. Krapels, “Target identification performance as a function of temporal and fixed pattern noise,” Opt. Eng. 40, 443-447 (2001).
    [CrossRef]

2007 (1)

R. Vollmerhausen and A. L. Robinson, “Modeling target acquisition tasks associated with security and surveillance,” Appl. Opt. 46, 4209-4221 (2007).
[CrossRef] [PubMed]

2005 (1)

R. H. Vollmerhausen, E. Jacobs, J. Hixson, and M. Friedman, “The targeting task performance (TTP) metric; a new model for predicting target acquisition performance,” Tech Report AMSEL-NV-TR-230, NVESD, US Army CERDEC, Fort Belvoir, Va., March 2005.

2004 (2)

P. G. J. Barten, “Formula for the contrast sensitivity of the human eye,” Proc. SPIE 5294, 231-238 (2004). Paper available on the Web at http://www.SPIE.org.
[CrossRef]

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43, 2806-2818 (2004).
[CrossRef]

2001 (1)

R. G. Driggers, R. Vollmerhausen, and K. Krapels, “Target identification performance as a function of temporal and fixed pattern noise,” Opt. Eng. 40, 443-447 (2001).
[CrossRef]

1995 (1)

R. H. Vollmerhausen, “Incorporating display limitations into night vision performance models,” IRIS Passive Sensors 2, 11-31 (1995).

Barten, P. G. J.

P. G. J. Barten, “Formula for the contrast sensitivity of the human eye,” Proc. SPIE 5294, 231-238 (2004). Paper available on the Web at http://www.SPIE.org.
[CrossRef]

Driggers, R.

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43, 2806-2818 (2004).
[CrossRef]

Driggers, R. G.

R. G. Driggers, R. Vollmerhausen, and K. Krapels, “Target identification performance as a function of temporal and fixed pattern noise,” Opt. Eng. 40, 443-447 (2001).
[CrossRef]

Friedman, M.

R. H. Vollmerhausen, E. Jacobs, J. Hixson, and M. Friedman, “The targeting task performance (TTP) metric; a new model for predicting target acquisition performance,” Tech Report AMSEL-NV-TR-230, NVESD, US Army CERDEC, Fort Belvoir, Va., March 2005.

Hixson, J.

R. H. Vollmerhausen, E. Jacobs, J. Hixson, and M. Friedman, “The targeting task performance (TTP) metric; a new model for predicting target acquisition performance,” Tech Report AMSEL-NV-TR-230, NVESD, US Army CERDEC, Fort Belvoir, Va., March 2005.

Jacobs, E.

R. H. Vollmerhausen, E. Jacobs, J. Hixson, and M. Friedman, “The targeting task performance (TTP) metric; a new model for predicting target acquisition performance,” Tech Report AMSEL-NV-TR-230, NVESD, US Army CERDEC, Fort Belvoir, Va., March 2005.

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43, 2806-2818 (2004).
[CrossRef]

Krapels, K.

R. G. Driggers, R. Vollmerhausen, and K. Krapels, “Target identification performance as a function of temporal and fixed pattern noise,” Opt. Eng. 40, 443-447 (2001).
[CrossRef]

Robinson, A. L.

R. Vollmerhausen and A. L. Robinson, “Modeling target acquisition tasks associated with security and surveillance,” Appl. Opt. 46, 4209-4221 (2007).
[CrossRef] [PubMed]

Vollmerhausen, R.

R. Vollmerhausen and A. L. Robinson, “Modeling target acquisition tasks associated with security and surveillance,” Appl. Opt. 46, 4209-4221 (2007).
[CrossRef] [PubMed]

R. G. Driggers, R. Vollmerhausen, and K. Krapels, “Target identification performance as a function of temporal and fixed pattern noise,” Opt. Eng. 40, 443-447 (2001).
[CrossRef]

Vollmerhausen, R. H.

R. H. Vollmerhausen, E. Jacobs, J. Hixson, and M. Friedman, “The targeting task performance (TTP) metric; a new model for predicting target acquisition performance,” Tech Report AMSEL-NV-TR-230, NVESD, US Army CERDEC, Fort Belvoir, Va., March 2005.

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43, 2806-2818 (2004).
[CrossRef]

R. H. Vollmerhausen, “Incorporating display limitations into night vision performance models,” IRIS Passive Sensors 2, 11-31 (1995).

R. H. Vollmerhausen, “Modeling the performance of imaging sensors,” in Electro-Optical Imaging: System Performance and Modeling, Chapter 12, L. Biberman, ed. (SPIE Press, Bellingham, Wash., 2000).

Appl. Opt. (1)

R. Vollmerhausen and A. L. Robinson, “Modeling target acquisition tasks associated with security and surveillance,” Appl. Opt. 46, 4209-4221 (2007).
[CrossRef] [PubMed]

IRIS Passive Sensors (1)

R. H. Vollmerhausen, “Incorporating display limitations into night vision performance models,” IRIS Passive Sensors 2, 11-31 (1995).

Opt. Eng. (2)

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43, 2806-2818 (2004).
[CrossRef]

R. G. Driggers, R. Vollmerhausen, and K. Krapels, “Target identification performance as a function of temporal and fixed pattern noise,” Opt. Eng. 40, 443-447 (2001).
[CrossRef]

Proc. SPIE (1)

P. G. J. Barten, “Formula for the contrast sensitivity of the human eye,” Proc. SPIE 5294, 231-238 (2004). Paper available on the Web at http://www.SPIE.org.
[CrossRef]

Other (2)

R. H. Vollmerhausen, E. Jacobs, J. Hixson, and M. Friedman, “The targeting task performance (TTP) metric; a new model for predicting target acquisition performance,” Tech Report AMSEL-NV-TR-230, NVESD, US Army CERDEC, Fort Belvoir, Va., March 2005.

R. H. Vollmerhausen, “Modeling the performance of imaging sensors,” in Electro-Optical Imaging: System Performance and Modeling, Chapter 12, L. Biberman, ed. (SPIE Press, Bellingham, Wash., 2000).

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Figures (13)

Fig. 1
Fig. 1

Comparison of erf function to empirical TTPF based on tactical vehicle ID experiments. Abscissa is V value (cycles on target) and ordinate is PID.

Fig. 2
Fig. 2

Example of face picture padded to prevent line spectra. This face is padded with white because of experimental procedure; normally, the face is padded with local background intensity (which in this case is black).

Fig. 3
Fig. 3

Twelve faces used in Moyer experiment.

Fig. 4
Fig. 4

Six aspects of each face used in Moyer experiment.

Fig. 5
Fig. 5

PID for individual faces; some faces are much easier to identify than others.

Fig. 6
Fig. 6

Plots of PID versus Φ for each individual in Moyer experiment. Open diamonds are data and solid line is model using Φ84 shown in individual plot.

Fig. 7
Fig. 7

Plot of PID (ordinate) versus range (abscissa) for the Hixson spotting scope experiment. Diamonds are data and solid line is model. Model PID is based on averaging individual PID.

Fig. 8
Fig. 8

Plot of PID (ordinate) versus noise level (abscissa) for the Vollmerhausen noise experiment. Diamonds are data and solid line is model. Model PID is based on averaging individual PID.

Fig. 9
Fig. 9

Plot shows PID (ordinate) versus range (abscissa) for Krapels naked eye experiment. Model is solid line and diamonds are data. The Model curve assumes that the faces used in the Krapels experiment are equivalent to the faces used in the Moyer experiment. PID predictions are based on averaging individual PID.

Fig. 10
Fig. 10

Moyer data compared to model fit. Model is average of probabilities generated by using FFT of each face and best fit Φ84 for that face. Error bars show maximum and minimum probabilities arising from using each individual face.

Fig. 11
Fig. 11

Model fit to Vollmerhausen data. Model line is average of probabilities generated using FFT of each face with associated Φ84 based on Moyer experiment. The error bars show maximum and minimum probabilities generated using individual face FFT and associated Φ84.

Fig. 12
Fig. 12

Model fit to Hixson data. Model line is average of probabilities generated using FFT of each face with Φ84 based on Moyer experiment. The error bars show maximum and minimum probabilities generated using individual face FFT and associated Φ84.

Fig. 13
Fig. 13

Model fit to Krapels data. Model line is average of probabilities generated using FFT of each face with Φ84 based on Moyer experiment. The error bars show maximum and minimum probabilities generated using individual face FFT and associated Φ84.

Equations (10)

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Φ = [ δ ( C T G T ( ξ , η , R n g ) C T F s y s ( ξ , η ) ) C T G T ( ξ , η , R n g ) C T F s y s ( ξ , η ) d ξ d η R n g 2 ] 1 / 2 .
C T F ( ξ , η ) = [ a f e b ( ξ 2 + η 2 ) 0.5 1 + 0.06 e b ( ξ 2 + η 2 ) 0.5 ] 1 ,
a = 540 ( 1 + 0.2 L ) 0.2 1 + 12 w 2 ( 1 + 5.8 ξ ) 2 , b = 5.24 ( 1 + 29.2 L ) 0.15 .
C T F s y s 2 ( ξ , η ) = C T F 2 ( ξ , η ) M T F 2 ( ξ , η ) [ 1 + α 2 σ 2 ( ξ , η ) ] .
δ ( C T G T ( ξ , η , R n g ) C T F s y s ( ξ , η ) ) = 1  for   C T G T ( ξ , η , R n g ) C T F s y s ( ξ , η ) 1
= 0  for   C T G T ( ξ , η , R n g ) C T F s y s ( ξ , η ) < 1 .
P I D ( Φ / Φ 84 ) = e r f ( Φ / Φ 84 ) = 2 π 0 Φ / Φ 84 e t 2 d t .
C T F s y s ( ξ , η ) = C T F ( ξ , η ) M T F ( ξ , η ) .
M T F s c o p e = e 0.0085 χ 1.5
χ = ( ξ 2 + η 2 ) 0.5 .

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