I have asked the same question on stackoverflow. I have been using OpenCV's traincascades to detect faces. Many time when the number of positive and negative sample are low, the training gets stuck after a stage where Hit Rate=1 and False Alarm=0 has been reached. So the command window output looks like this-
===== TRAINING 13-stage =====
<BEGIN
POS count : consumed 90 : 90
NEG count : acceptanceRatio 300 : 3.46407e-006
Precalculation time: 0.029
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 0|
+----+---------+---------+
END>
Training until now has taken 0 days 0 hours 17 minutes 26 seconds.
===== TRAINING 14-stage =====
<BEGIN
POS count : consumed 90 : 90
I am guessing subsequent negatives are rejected and training is stuck in infinite loop- for( ; ; ) of the traincascade. If I create a cascade of the intermediate stages I get an average classifier (accuracy-60-70%). There has been another similar question for the same problem but I felt that the answer wasn't good enough.
Is there any solution to this problem?