TY - GEN
T1 - Do you see what i see? A more realistic eyewitness sketch recognition
AU - Nejati, Hossein
AU - Sim, Terence
AU - Martinez-Marroquin, Elisa
PY - 2011
Y1 - 2011
N2 - Face sketches have been used in eyewitness testimonies for about a century. However, 30 years of research shows that current eyewitness testimony methods are highly unreliable. Nonetheless, current face sketch recognition algorithms assume that eyewitness sketches are reliable and highly similar to their respective target faces. As proven by psychological findings and a recent work on face sketch recognition, these assumptions are unrealistic and therefore, current algorithms cannot handle real world cases of eyewitness sketch recognition. In this paper, we address the eyewitness sketch recognition problem with a two-pronged approach. We propose a more reliable eyewitness testimony method, and an accompanying face sketch recognition method that accounts for realistic assumptions on sketch-photo similarities and individual eyewitness differences. In our eyewitness testimony method we first ask the eyewitness to directly draw a sketch of the target face, and provide some ancillary information about the target face. Then we build a drawing profile of the eyewitness by asking him/her to draw a set of face photos. This drawing profile implicitly contains the eyewitness' mental bias. In our face sketch recognition method we first correct the sketch for the eyewitness' bias using the drawing profile. Then we recognize the resulting sketch based on an optimized combination of the detected features and ancillary information. Experimental results show that our method is 12 times better than the leading competing method at Rank-1 accuracy, and 6 times better at Rank-10. Our method also maintains its superiority as gallery size increases.
AB - Face sketches have been used in eyewitness testimonies for about a century. However, 30 years of research shows that current eyewitness testimony methods are highly unreliable. Nonetheless, current face sketch recognition algorithms assume that eyewitness sketches are reliable and highly similar to their respective target faces. As proven by psychological findings and a recent work on face sketch recognition, these assumptions are unrealistic and therefore, current algorithms cannot handle real world cases of eyewitness sketch recognition. In this paper, we address the eyewitness sketch recognition problem with a two-pronged approach. We propose a more reliable eyewitness testimony method, and an accompanying face sketch recognition method that accounts for realistic assumptions on sketch-photo similarities and individual eyewitness differences. In our eyewitness testimony method we first ask the eyewitness to directly draw a sketch of the target face, and provide some ancillary information about the target face. Then we build a drawing profile of the eyewitness by asking him/her to draw a set of face photos. This drawing profile implicitly contains the eyewitness' mental bias. In our face sketch recognition method we first correct the sketch for the eyewitness' bias using the drawing profile. Then we recognize the resulting sketch based on an optimized combination of the detected features and ancillary information. Experimental results show that our method is 12 times better than the leading competing method at Rank-1 accuracy, and 6 times better at Rank-10. Our method also maintains its superiority as gallery size increases.
UR - http://www.scopus.com/inward/record.url?scp=84856103296&partnerID=8YFLogxK
U2 - 10.1109/IJCB.2011.6117497
DO - 10.1109/IJCB.2011.6117497
M3 - Conference contribution
AN - SCOPUS:84856103296
SN - 9781457713583
T3 - 2011 International Joint Conference on Biometrics, IJCB 2011
BT - 2011 International Joint Conference on Biometrics, IJCB 2011
T2 - 2011 International Joint Conference on Biometrics, IJCB 2011
Y2 - 11 October 2011 through 13 October 2011
ER -