Current head-mounted wearable sensors for monitoring of food intake operates by fusing multiple modalities such as inertial and image sensing. The image capture may be performed periodically, capturing a large number of irrelevant images, increasing power consumption and reducing the battery life. In this manuscript, we propose an efficient approach for food image capture, that captures the images only when the head tilt angle estimated from the accelerometer data matches that during ingestion of food. The method was developed and validated using data from 15 volunteers consuming unrestricted meals in a free-living environment between 12.5 to 18.5 hours. The tilt angle of the head was computed using 3D accelerometer data. A classifier for image capture was developed using a curve fitting approach on the tilt angles of the head. The proposed method achieved a sensitivity of 0.97 and specificity of 0.47 in predicting capture of food images, thus potentially improving the battery life of the wearable device.