zg&@ &&#TNPP2OMi & TNPP &&TNPP   @ - "-- !@-- "-&; 7& - & 7& &"@J&--- $"@*@*J"J $@@JJ - $*@2@2J*J $@@JJ- $2@:@:J2J $@@JJ- $:@B@BJ:J $@@JJ- $B@J@JJBJ $@@JJ- $J@R@RJJJ $@@JJ"""- $R@Z@ZJRJ $@@JJ'''- $Z@b@bJZJ $@@JJ+++- $b@j@jJbJ $@@JJ///- $j@r@rJjJ $@@JJ333- $r@z@zJrJ $@@JJ888- $z@@JzJ $@@JJ===- $@@JJ $~@@J~JAAA- $@@JJ $v@~@~JvJEEE- $@@JJ $n@v@vJnJJJJ- $@@JJ $f@n@nJfJNNN- $@@JJ $^@f@fJ^JSSS- $@@JJ $V@^@^JVJWWW- $@@JJ $N@V@VJNJ[[[- $@@JJ $F@N@NJFJ___- $@@JJ $>@F@FJ>Jddd- $@@JJ $6@>@>J6Jhhh- $@@JJ $.@6@6J.Jkkk- $@@JJ $&@.@.J&Jooo- $@@JJ $@&@&JJrrr- $@@JJ $@@JJvvv- $@@JJ $@@JJyyy- $@@JJ $@@JJ{{{- $@ @ JJ $@@JJ~~~- $ @@J J $@@JJ- $@@JJ $@@JJ- $@"@"JJ $@@JJ- $"@*@*J"J $@@JJ- $*@2@2J*J $@@JJ- $2@:@:J2J $@@JJ- $:@B@BJ:J $@@JJ- $B@J@JJBJ $@@JJ- $J@R@RJJJ $@@JJ- $R@Z@ZJRJ $@@JJ- $Z@b@bJZJ $@@JJ- $b@j@jJbJ $@@JJ $j@r@rJjJ $@@JJ- $r@z@zJrJ $@@JJ $z@@JzJ $@@JJ- $@@JJ---&&&_ "--%`--&---- @Times New Roman- . 2 1.--A@-- @Times New Roman- .2 5HAbstract  .--k&-- @Times New Roman- .c2 <=For a computer vision system, the task of recognizing human f                . .32 aces from single pictures is       . ."2 -made difficult by     .@Times New Roman - .2 variations   .@Times New Roman - .f2 ?in face position, size, expression, and pose (front, profile, .              . .2 ..). . .g2 -@We present an automatic system able to recognize human faces on                 . .$2 1the basis of single    . . 2 grey  . . 2 -. .2 - level mug  . . 2 y-. .g2 @shots matched against a large data set including one image per p               . .2 n erson (100  . . 2 -. . 2 250 . .g2 -@persons). The system performance and robustness is assessed, and             . .*2 /is compared with other       . .2 -systems. . .?2 "<%2D views of faces are represented by          .@Times New Roman - .2 "\labeled graphs    .@Times New Roman - .72 " . Graph nodes are labeled with      .@Times New Roman - . 2 "jets .@Times New Roman- .f2 9-?and graph edges are labeled with distance vectors. Jets are 80             . . 2 9 -. ..2 9dimensional vectors based        . .2 P-on a 2D  .@Times New Roman - .2 PnGabor . . 2 P-. .!2 Pwavelet transform    .@Times New Roman - .%2 P@. We use 40 complex     . .2 PGabor . . 2 P-. .%2 P#wavelets, which are    . .g2 g-@localized filters, each of a certain spatial frequency and orien                . .32 gtation (8 orientations and 5          . .g2 ~-@spatial frequencies). Jets are a concise and robust representat         . .2 ~ ion of local   . . 2 ~bgrey  . . 2 ~-. .2 ~ level value    . .g2 -@regions of the image. Since we want to compare faces across pose                . .02 ), we define a set of about       . .2 - 45 facial (or    .@Times New Roman - .2 fiducial  .@Times New Roman - .g2 @) points at which nodes are positioned. These points are identic              . .2 al in  . .g2 -@different poses (to the extent they are visible). Points such as                . .92 !the tip of the nose, the corners         . .g2 -@of the mouth, the pupils, the tip of the chin are included. The                 . .02  graphs thus have an object        . .g2 -@adapted grid and compatible nodes in different views can be comp                     . .*2 ,ared with each other.      .-- "System-&TNPP &