METHODS: A total of 144 participants were recruited (48 cigarette smokers, 48 e-cigarette smokers, and 48 never-smokers). Clinical periodontal parameters, including plaque index (PI), gingival index (GI), periodontal probing pocket depth (PPD), and clinical attachment loss (CAL) were recorded, excluding third molars. The level of unstimulated whole salivary pH was measured using a portable pH meter and the levels of salivary cotinine were measured using Enzyme-Linked Immunosorbent Assay (ELISA).
RESULTS: Data were analysed statistically using analysis of variance. Mean scores of PPD, percentage of pocket depth ≥ 4 mm, and CAL (p
OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.
RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.
CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.