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.
METHOD: A convenience sample of 102 patients was recruited from four Cure and Care Service Centres in Malaysia.
RESULTS: Principal component analysis with varimax rotation supported two-factor solutions for each subscale: problem recognition, desire for help and treatment readiness, which accounted for 63.5%, 62.7% and 49.1% of the variances, respectively. The Cronbach's alpha coefficients were acceptable for the overall measures (24 items: ∝ = 0.89), the problem recognition scale (10 items; ∝ = 0.89), desire for help (6 items; ∝ = 0.64) and treatment readiness scale (8 items; ∝ = 0.60). The results also indicated significant motivational differences for different modalities, with inpatients having significantly higher motivational scores in each scale compared to outpatients.
CONCLUSION: The present study pointed towards the favourable psychometric properties of a motivation for treatment scale, which can be a useful instrument for clinical applications of drug use changes and treatment.