A gesture-based interaction system for smart homes is a part of a complex cyber-physical environment, for which researchers and developers need to address major challenges in providing personalized gesture interactions. However, current research efforts have not tackled the problem of personalized gesture recognition that often involves user identification. To address this problem, we propose in this work a new event-driven service-oriented framework called gesture services for cyber-physical environments(GS-CPE) that extends the architecture of our previous work gesture profile for web services(GPWS). To provide user identification functionality, GS-CPE introduces a two-phase cascading gesture password recognition algorithm for gesture-based user identification using a two-phase cascading classifier with the hidden Markov model and the Golden Section Search, which achieves an accuracy rate of 96.2% with a small training dataset. To support personalized gesture interaction, an enhanced version of the Dynamic Time Warping algorithm with multiple gestural input sources and dynamic template adaptation support is implemented. Our experimental results demonstrate the performance of the algorithm can achieve an average accuracy rate of 98.5% in practical scenarios. Comparison results reveal that GS-CPE has faster response time and higher accuracy rate than other gesture interaction systems designed for smart-home environments. A gesture-based interaction system for smart homes is a part of a complex cyber-physical environment, for which researchers and developers need to address major challenges in Providing personalized gesture interactions. However, current research efforts have not tackled the problem of personalized gesture recognition that often involves user identification. To address this problem, we propose in this work a new event-driven service-oriented framework called gesture services for cyber-physical environments (GS-CPE) that extends the architecture of our previous work gesture profile for web services (GPWS). To provide user identification functionality, GS-CPE introduces a two-phase cascading gesture password recognition algorithm for gesture-based user identification using a two-phase cascading classifier with the hidden Markov model and the Golden Section Search, which achieves an accuracy rate of 96.2% with a small training dataset. To support personalized gesture interaction, an enhanced version of the Dynamic Time Warping algorithm with multiple gestural input sources and dynamic template adaptation support is implemented. Our experimental results demonstrate the performance of the algorithm can achieve an average accuracy rate of 98.5% in practical scenarios. Comparison results reveal that GS-CPE has faster response time and higher accuracy rate than other gesture interaction systems designed for smart-home environments.