Innovative Information Technology-Based Teaching Methods for Fingerprint Identification in Criminal Technology Programs and Teacher Skill Development

Figure 1.

Figure 2.

Figure 3.

Figure 4.

The design of fingerprint recognition of students in the 2022 and 2023 students

Serial number	Project	Coincidence number		Gap
		2022	2023
1	To summarize the experience of each experiment	112	137	25
2	Note the operation points involved in the experiment	124	143	19
3	Observe and summarize all kinds of experiments	119	140	21
4	The same electrical classification and a special section of knowledge	125	139	14
5	With problems, you can seek support from others	117	142	25
6	Apply the test strategy and review your knowledge	123	140	17

The goal of the 2022 and 2023 students is “criminal technology_”

Serial number	Project	Coincidence number		Gap
		2022	2023
1	Basic concepts, knowledge, and principles are well mastered	112	135	23
2	Excellent test performance	121	143	22
3	Flexible application of knowledge, analysis of case	118	144	26
4	The on-site comprehensive examination results are good	116	138	22
5	Ability to identify and filter information	124	136	12
6	Can find, ask questions	113	141	28
7	The ability to solve practical problems and gradually develop the ability to think	119	142	23
8	Basic skills can be mastered, and the relevant technologies can be completed independently	124	140	16
9	Flexible use of learning to solve practical problems has a successful career experience	121	139	18
10	Written examination results are good	113	135	22
11	Operational skills and theoretical knowledge can be closely related	115	139	24

The questionnaire distribution of students’ learning state in criminal technology

Teaching code	Effective questionnaire	Subordinate major	At the time of this course	Questionnaire statistics
22-XZ-1	72	Criminal investigation	2022-03-07	145 copies (2022)
22-ZA-2	73	Security management	2022-03-07
23-XZ-1	72	Criminal investigation	2023-03-07	144 copies (2023)
23-ZX-2	72	Security management	2023-03-07

Algorithm execution time (unit:ms)

Algorithm scenario		I		II		III
Translation velocity	Rotational speed	Scene 1	Scene2	Scene 1	Scene 2	Scene 1	Scene2
5	100	13.56	14.24	19.57	18.65	0.89	0.83
	150	21.54	20.16	17.64	19.85	0.89	0.91
	200	14.65	18.78	23.21	17.03	0.91	0.83
	250	25.34	41.32	25.36	29.34	0.93	0.92
	300	14.56	13.68	20.73	21.32	0.86	0.85
10	100	16.85	18.69	15.64	10.05	0.86	0.83
	150	13.24	15.86	16.28	19.58	0.82	0.89
	200	19.86	12.53	18.76	16.42	0.93	0.89
	250	12.32	12.26	18.96	16.65	0.93	0.98
	300	16.58	17.23	20.68	15.16	0.86	0.82
20	100	36.25	30.75	14.65	23.98	0.93	0.82
	150	50.65	42.36	10.32	14.24	0.85	0.92
	200	44.65	55.25	13.65	15.46	0.85	0.89
	250	16.34	60.42	23.36	29.64	0.94	0.82
	300	78.93	110.31	18.55	16.32	0.95	0.83
30	100	65.36	77.05	17.83	12.93	0.84	0.89
	150	96.21	66.35	16.75	20.04	0.93	0.88
	200	75.62	115.21	18.32	11.37	0.85	0.86
	250	77.58	76.85	14.96	18.54	0.89	0.79
	300	88.04	87.23	18.98	19.65	0.84	0.93