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First, the number of points depends always on your texture. SIFT/SURF extract their features in corner-like image structures. So no corners, no/less feature points of poor quality.
In general, there is no hard border for match / non match. You may set a specific threshold but this wilk not give you robust results. A solution is to work with probabilities. Imagine a histogramm which tells you "model A gets 20 matches", "model B gets 2 matches" and "model C gets 3 matches". In this case, your distribution will tell you what will be a good match. Also try to look for terms like "bag of words classifier", "k-means" etc. OpenCV will help you with this.
