System for and method of securing fingerprint biometric systems against fake-finger spoofing

1. A biometric secured system comprising:

a fingerprint sensor for capturing fingerprint image data; and

a spoof detection module programmed to determine a spoof probability from a combination of metrics derived from the fingerprint image data, wherein the combination of metrics comprise any two or more of pixel gray level average and variance of pixels corresponding to a fingerprint ridge, pixel gray level average and variance of pixels corresponding to a fingerprint valley, density of sweat pores, density of streaks, and raw signal level.

2. The secured system of claim 1 , wherein the spoof detection module comprises:

a metric generator for generating the metrics; and

classifier logic programmed to generate from the metrics a raw probability that the fingerprint image data was generated from a synthetic material.

3. The secured system of claim 1 , wherein the combination of metrics further comprise values corresponding to any one or more of ridge elasticity of a fingerprint image, electrical properties of a fingerprint, optical properties of a fingerprint, and vitality properties of a fingerprint.

4. The secured system of claim 2 , wherein the spoof detection module also comprises an adjustor programmed to adjust the raw probability by a base probability to generate the spoof probability.

5. The secured system of claim 4 , wherein the base probability is generated from stored metrics.

6. The secured system of claim 5 , wherein the stored metrics are based on fingerprint image data captured during an enrollment step.

7. The secured system of claim 2 , wherein the spoof detection module also comprises a filter for dividing the fingerprint sensor into multiple windows, and the classifier logic is also programmed to determine the spoof probability based on a comparison of the values of the metric in each of the multiple windows.

8. The secured system of claim 7 , wherein dimensions of each of the windows are programmable.

9. The secured system of claim 2 , wherein the classifier logic comprises a neural network for learning to generate the raw probability from the combination of metrics.

10. The secured system of claim 1 , wherein the fingerprint sensor comprises a swipe sensor.

11. The secured system of claim 1 , wherein the fingerprint sensor comprises a placement sensor.

12. The secured system of claim 1 , further comprising an access module for granting access to a host system when the spoof probability is within a predetermined range.

13. The secured system of claim 12 , further comprising a host system coupled to the access module.

14. The secured system of claim 13 , wherein the host system comprises any one of a cell phone, a personal digital assistant, a digital camera, and a personal computer.

15. The secured system of claim 5 , further comprising a storage for storing the stored metrics.

16. The secured system of claim 15 , wherein the storage is coupled to the spoof detection module over a network.

17. The secured system of claim 5 , wherein the stored metrics are encrypted.

18. A spoof detection module comprising a computer readable medium having computer readable program logic recorded thereon for controlling a processor, the logic comprising:

a metric calculator programmed to receive fingerprint image data and calculate a combination of multiple metrics; and

classifier logic programmed to receive the multiple metrics and determine a spoof probability based on the combination of the multiple metrics, wherein the multiple metrics comprise any two or more of pixel gray level average and variance of pixels corresponding to a fingerprint ridge, pixel gray level average and variance of pixels corresponding to a fingerprint valley, density of sweat pores. density of streaks, and raw signal level.

19. The spoof detection module of claim 18 , wherein the multiple metrics further comprise values corresponding to any one or more of ridge elasticity of a fingerprint, electrical properties of a fingerprint, optical properties of a fingerprint, and vitality properties of a fingerprint.

20. The spoof detection module of claim 18 , wherein at least two of the multiple metrics are interdependent.

21. The spoof detection module of claim 18 , further comprising a probability adjustor programmed to receive the spoof probability and adjust it based on stored fingerprint data to generate an adjusted spoof probability.

22. The spoof detection module of claim 18 , further comprising a filter for dividing the fingerprint sensor into multiple windows, wherein the classifier logic is also programmed to determine the spoof probability based on a comparison of the values of the metric in each of the multiple windows.

23. The spoof detection module of claim 22 , wherein dimensions of each of the windows is programmable.

24. The spoof detection module of claim 18 , wherein the classifier logic comprises a neural network for learning to generate the spoof probability from the multiple metrics.

25. The spoof detection module of claim 18 , wherein the fingerprint image data corresponds to data captured using a fingerprint swipe sensor.

26. The spoof detection module of claim 18 , wherein the fingerprint image data corresponds to data captured using a fingerprint placement sensor.

27. A method of determining the similarity of a finger to human skin, comprising:

capturing fingerprint image data; and

determining a spoof probability that the finger is real based on a combination of metrics derived from the fingerprint image data, wherein the combination of metrics comprise any two or more of pixel gray level average and variance of pixels corresponding to a fingerprint ridge, pixel gray level average and variance of pixels corresponding to a fingerprint valley, density of sweat pores, density of streaks, and raw signal level.

28. The method of claim 27 , wherein the combination of metrics further includes density of streaks, the method further comprising dividing the fingerprint image data into multiple windows, wherein the spoof probability is based on a comparison of the values of the metric in each of the multiple windows.

29. The method of claim 27 , wherein the combination of metrics further comprise values corresponding to any one or more of ridge elasticity of a fingerprint, electrical properties of a finger, optical properties of a finger, and vitality properties of a finger.

30. The method of claim 27 , wherein at least two of the metrics from the combination of metrics are interdependent.

31. The method of claim 27 , further comprising adjusting the spoof probability using fingerprint image data captured during an enrollment step.

32. The method of claim 31 , further comprising encrypting the fingerprint image data captured during the enrollment step.

33. The method of claim 31 , further comprising determining the spoof probability at a first location and storing the fingerprint image data captured during an enrollment step at a second location, wherein the first location and the second location are coupled over a network.

34. The method of claim 27 , further comprising using a neural network for learning to compute the spoof probability from the multiple metrics.

35. The method of claim 27 , wherein the fingerprint image data is captured using a swipe sensor.

36. The method of claim 27 , wherein the fingerprint image data is captured using a placement sensor.

37. The method of claim 27 , wherein a threshold for the spoof probability depends on a level of a transaction.

38. The spoof detection module of claim 18 , wherein the computer readable program logic comprises at least one of computer instructions and application-specific integrated circuits.