## 4 Methodologies and Results Analysis
### Final Phase
Face hidden with (**a**) black plastic, (**b**) black plastic and in motion, (**c**) face captured on a dark night, (**d**) storage samples of captured intruder’s image and a compressed video with a timestamp, and (**e**) a customized mobile app with samples of ongoing theft notification (copyright permission was obtained for all of the human faces shown on 11 May 2017). Figure 15: _Cont._  
Figure 16a depicts the correct face detection rate analysis against 10,000 images of humans detected at night by an ordinary analog camera without night vision capability. Faces were accurately detected in approximately 67% of cases, with similar accuracy achieved for high numbers of images. Figure 16b represents face detection rate analysis of detected 67% cases in form of the heat map representation. Figure 16: **(a)** Face detection rate analysis of the proposed system with 10,000 face samples in scenarios where an intruder has been detected in the dark night. **(b)** Heat map representation of face detection rate analysis of the proposed system with 10,000 face samples in scenarios where an intruder has been detected in the dark night. Figure 15: **(a)** Face detection rate analysis of the proposed system with 10,000 face samples in scenarios where an intruder has a fully covered face, as per Figure 9. **(b)** Heat map representation of face detection rate analysis of the proposed system with 10,000 face samples in scenarios where an intruder has a fully covered face. Analysis of the male and female ratio used in the conducted rigorous research experiments in all the above-mentioned scenarios is presented in Figure 17, which depicts a variety of male and female samples in the following scenarios: (a) face not obscured, (b) face partially covered, (c) face fully covered, and (d) captured in the dark by an analog camera.The total sample size used for all the research experiments was 10,000 images. Figure 17: Cont. An analysis of various skin tones: dark, whitish-brown, whitish, fair, very fair, and white is given in Figure 18. The proposed system achieved high accuracy for all varieties of skin tones and for all the above-mentioned scenarios, as shown in Table 3. It clearly depicts that the conducted experiments on a variety of skin tones such as white, very fair, fair, wheatish-brown, wheatish, and dark achieved above 80% accuracy when an intruder has partially hidden their face, above 70% accuracy when an intruder has fully hidden their face with some type of plastic, leather, or paper, and above 60% accuracy when an intruder was captured in the dark night with an ordinary analog camera without night vision capability. Figure 17: Analysis of male–female ratio where the face is (**a**) face not obscured, (**b**) face partially covered, (**c**) face fully covered, and (**d**) captured in the dark by an analog camera. Figure 18: Detailed Analysis of various skin tones samples used in scenarios: face not obscured, face partially covered, face fully covered, and captured in the dark by an analog camera. Analysis of the various age groups used in the conducted rigorous research experiments in all the above-mentioned scenarios is presented in Figure 20, which depicts a variety of age group samples: (i) <10 years, (ii) between (10-15 years), (iii) between (15-20 years), (iv) between (20-35 years), (v) between (35-45 years), (vi) between (45-55 years), (vii) between (55-60 years), and (viii) >60 against corresponding face detection rate and face samples. The total sample size used for all the research experiments was 10,000 images. Table 4 represents a face detection rate accuracy analysis of the modified Haar Cascade Classifier with existing methodologies in four scenarios: (a) face not obscured, (b) face partially covered, (c) face fully covered, and (d) captured in the dark by an analog camera.