Driver Drowsiness and Alertness Warning System (DDAWS) is a Driver In Loop (DIL) system, where the driver alertness state is monitored in real time condition and an attention warning alert is prompted if the driver is detected to be drowsy. Presently, DDAWS regulation (AIS 184) is under discussion for implementation in India while it is already being featured in the vehicle models with various names, references and strategies. The upcoming DDAWS regulation refers the Karolinska Sleepiness Scale (KSS) or its equivalent only as alertness input. Driver alertness is monitored majorly by three techniques namely Physiological data (ECG, EEG etc.), Facial Features & Behavior recognition (Image processing) and Vehicle Metrics (Vehicle behavior and Driver inputs etc.). Image Processing technique is widely deployed in automotive applications for featuring the DDAWS with no standard references of inputs for process and output. Therefore, the paper talks about quantification of alertness for Image processing technique as per KSS so that the basic input could be standardized and facial features & behavior recognition could be read in a standard manner. The methodology defines the threshold values of Blink, Yawn and Head pose and these are defined by number of blinks with classification of heavy blink and light blink, number of Yawns and Head pose in (X, Y, Z) directions respectively. KSS stage is calculated using the selected frames of data. The protocols and trigger functions are defined for each stage of KSS. Also the paper talks about recommendations on provision of secondary strategies based on vehicle metrics like Standard Lane Deviation laterally (SDLAT), Yaw rate, latest activation of controls etc., confirming the primary warning signal. In conclusion, the paper addresses the quantification of alertness and evaluation methodology very close to reality condition on image processing technique based DDAW system for the standard AIS 184.