Presentation on theme: "Biometrics & Security Tutorial 9. 1 (a) What is palmprint and palmprint authentication? (P10: 9-10)"— Presentation transcript:
Biometrics & Security Tutorial 9
1 (a) What is palmprint and palmprint authentication? (P10: 9-10)
1 (b) What kinds of features can be extracted from a palm? (P10:11)
1 (c) How to get Datum Point in P10: 14?
1 (d) Given two lines in a palmprint image, please explain how to get their Euclidean distance (P10: 21). If we mark each line as orientation features, how to understand their matching score: S(C1, C2) in P10:33?
1 (e) What are the advantages and disadvantages of palmprint recognition? (P10: 58)
2. In line feature based offline palmprint recognition, four different directional templates are defined. Please try to explain why they can detect the corresponding directional line segments (See P10:19). Can you design another set of four different directional templates to determine line segments?
A line segment Its cross-section Our line detector Its cross-section 121 The inner product of two vectors increases with their similarity !
Line detector Zero sum Similar cross-section to a line segment Larger than a line segment The better similarity, the better detection How many directions we need? The more directions, the more accuracy, the less efficiency A tradeoff is made according to experience
3. In P10:28-29, a segmentation approach, Tangent Points of the Finger Hole, is introduced. The line passing though the two points, (x1, y1) and (x2, y2), satisfies the inequality, for all i and j. Please understand this method and check the determinant rule.
4. Assume that there is a Palmprint Code with 256 bytes by using texture feature. Please compute the number of features represented by the Palmprint Code (See P10:36). (256 * 8 / 2 == 1,024)
Tut 7, 2. There are four steps in the Daugman’s approach (P8: 32-36). The third step generates IrisCode with 512 bytes. If 2 bits represent a feature, please compute the total number of features. (512*(8/2)=2,048)
5. In texture features matching stage, hamming distance is used to measure the similarity of two palmprints, as shown in P10:37. What conclusion can you get from the normalized distance D 0 ? Hint: Consider the normalized distance with the experimental results in P10:38. (The smaller in the normalized distance, the closer in two palmprints)