DESC:STATIC POSTURE BASED PERSON IDENTIFICATION ,CLAIMS:1. A method for identifying an unknown person based on a static posture of the unknown person, the method comprising:
receiving data of N skeleton joints of the unknown person, wherein the data of the N skeleton joints is received from a skeleton recording device (104);
identifying, by a processor (108), the static posture of the unknown person;
extracting, by the processor (108), a static posture feature set corresponding to the static posture of the unknown person for identification of the unknown person, wherein the static posture feature set is extracted based on the data of the N skeleton joints of the unknown person; and
identifying, by the processor (108), the unknown person as one from amongst a plurality of known persons based on comparison of the static posture feature set for the unknown person with training static posture feature sets for the plurality of known persons and corresponding to the static posture.

2. The method as claimed in claim 1, wherein the N skeleton joints of the unknown person comprises a head joint, a shoulder centre joint, a shoulder left joint, a shoulder right joint, a spine joint, a hand left joint, a hand right joint, an elbow right joint, an elbow left joint, a wrist right joint, a wrist left joint, a hip left joint, a hip right joint, a hip centre joint, a knee right joint, a knee left joint, a foot left joint, a foot right joint, an ankle right joint, and an ankle left joint.

3. The method as claimed in claim 1 further comprising:
determining joint coordinates of the N skeleton joints of the unknown person, wherein the joint coordinates comprise Cartesian joint coordinates and spherical joint coordinates of each of the N skeleton joints, and
wherein the static posture feature set is extracted based on the joint coordinates.

4. The method as claimed in claim 1, wherein the static posture of the unknown person is identified as a predefined static posture based on joint coordinates of predefined skeleton joints, from amongst the N skeleton joints, of the unknown person.

5. The method as claimed in claim 4, wherein the predefined static posture is one of a sitting posture and a standing posture.

6. The method as claimed in claim 1, wherein, when the unknown person is identified to be in a sitting posture, the static posture feature set for the unknown person is a sitting feature set, and the training static posture feature sets are training sitting feature sets of the plurality of known persons.

7. The method as claimed in claim 6, wherein the sitting feature set comprises a first set of static features, and wherein the first set of static features comprises angle between a shoulder left joint, a shoulder centre joint, and a spine joint, angle between a shoulder right joint, the shoulder centre joint, and a spine joint, angle between the shoulder centre joint and the spine with respect to a vertical axis, area occupied by a polygon formed by the shoulder left joint, the shoulder centre joint, and the shoulder right joint, and a distance between two joints in each of a Cartesian co-ordinate system and a spherical co-ordinate system.

8. The method as claimed in claim 1, wherein, when the unknown person is identified to be in the standing posture, the static posture feature set for the unknown person is a standing feature set, and the training static posture feature sets are training standing feature sets of the plurality of known persons.

9. The method as claimed in claim 8, wherein the standing feature set comprises a second set of static features, and wherein the second set of static features comprises an angle between a shoulder left joint, a shoulder centre joint, and a spine joint, an angle between a shoulder right joint, the shoulder centre joint, and the spine joint, an angle between the shoulder centre joint and the spine with respect to a vertical axis, an angle between a hip left joint, a hip centre joint, and a hip right joint, an area occupied by a polygon formed by the shoulder left joint, the shoulder centre joint, and the shoulder right joint, an area occupied by a polygon formed by the hip left joint, the hip centre joint, and the hip right joint, and a distance between two joints in each of a Cartesian co-ordinate system and a spherical co-ordinate system.

10. The method as claimed in claim 4, wherein the method further comprising:
receiving data of N skeleton joints of each of the plurality of known persons for the predefined static posture, wherein the data of the N skeleton joints is received from the skeleton recording device (104);
extracting a training static posture feature set for each of the plurality of known persons based on the data of N skeleton joints of a respective known person; and
storing the training static posture feature set for each of the plurality of known persons to identify the unknown person, from amongst the plurality of known persons.

11. A person identification system (102) for identifying an unknown person based on a static posture of the unknown person, the person identification system (102) comprising:
a processor (108);
a skeleton data processing module (118) coupled to, and executable by, the processor (108) to,
receive data of N skeleton joints of the unknown person from a skeleton recording device (104); and
determine joint coordinates of the N skeleton joints of the unknown person;
a posture detection module (120) coupled to, and executable by, the processor (108) to,
identify the static posture of the unknown person;
an identification module (122) coupled to, and executable by, the processor (108) to,
extract a static posture feature set for the unknown person corresponding to the static posture of the unknown person based on the joint coordinates of the N skeleton joints of the unknown person; and
identify the unknown person as one from amongst a plurality of known persons based on comparison of the static posture feature set for the unknown person with training static posture feature sets for the plurality of known persons and corresponding to the static posture.

12. The person identification system (102) as claimed in claim 11, wherein the skeleton data processing module (118) further determines spherical joint coordinates and Cartesian joint coordinates of each of the N skeleton joints of the unknown person.

13. The person identification system (102) as claimed in claim 11, wherein the posture detection module (120) identifies the static posture of the unknown person as a predefined static posture based on joint coordinates of predefined skeleton joints, from amongst the N skeleton joints, of the unknown person, and wherein the predefined static posture is one of a sitting posture and a standing posture.

14. The person identification system (102) as claimed in claim 11, when the unknown person is identified to be in a sitting posture, the static posture feature set for the unknown person is a sitting feature set, and the training static posture feature sets are training sitting feature sets of the plurality of known persons.

15. The person identification system (102) as claimed in claim 14, wherein the sitting feature set comprises a first set of static features, and wherein the first set of static features comprises an angle between a shoulder left joint, a shoulder centre joint, and a spine joint, an angle between a shoulder right joint, the shoulder centre joint, and a spine joint, an angle between the shoulder centre joint and the spine with respect to a vertical axis, an area occupied by a polygon formed by the shoulder left joint, the shoulder centre joint, and the shoulder right joint, and a distance between two joints in each of a Cartesian co-ordinate system and a spherical co-ordinate system.

16. The person identification system (102) as claimed in claim 11, when the unknown person is identified to be in the standing posture, the static posture feature set for the unknown person is a standing feature set, and the training static posture feature sets are training standing feature sets of the plurality of known persons.

17. The person identification system (102) as claimed in claim 16, wherein the standing feature set comprises a second set of static features, and wherein the second set of static features comprises an angle between a shoulder left joint, a shoulder centre joint, and a spine joint, an angle between a shoulder right joint, the shoulder centre joint, and the spine joint, an angle between the shoulder centre joint and the spine with respect to a vertical axis, an angle between a hip left joint, a hip centre joint, and a hip right joint, an area occupied by a polygon formed by the shoulder left joint, the shoulder centre joint, and the shoulder right joint, an area occupied by a polygon formed by the hip left joint, the hip centre joint, and the hip right joint, and a distance between two joints in each of a Cartesian co-ordinate system and a spherical co-ordinate system.

18. The person identification system (102) as claimed in claim 11, wherein the skeleton data processing module (118) further:
receives data of N skeleton joints of each of the plurality of known persons for a predefined static posture, wherein the data of N skeleton joints is received from a skeleton recording device (104), and wherein the predefined static posture is one of a sitting posture and a standing posture;
extracts a training static posture feature set for each of the plurality of known persons based on the data of N skeleton joints of a respective known person; and
stores the training static posture feature set for each of the plurality of known persons to identify the unknown person, from amongst the plurality of known persons.

19. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method comprising:
receiving data of N skeleton joints of an unknown person, wherein the data of the N skeleton joints is received from a skeleton recording device (104);
identifying the static posture of the unknown person;
extracting a static posture feature set corresponding to the static posture of the unknown person, wherein the static posture feature set is extracted based on the data of the N skeleton joints of the unknown person; and
identifying the unknown person as one from amongst a plurality of known persons based on comparison of the static posture feature set for the unknown

person with training static posture feature sets for the plurality of known persons and corresponding to the static posture.