Gaze estimation plays a significant role in understating human behavior and in human–computer interaction. Currently, there are many methods accessible for gaze estimation. However, most approaches need additional hardware for data acquisition which adds an extra cost to gaze tracking. The classic gaze tracking approaches usually require systematic prior knowledge or expertise for practical operations. Moreover, they are fundamentally based on the characteristics of the eye region, utilizing infrared light and iris glint to track the gaze point. It requires high-quality images with particular environmental conditions and another light source. Recent studies on appearance-based gaze estimation have demonstrated the capability of neural networks, especially convolutional neural networks (CNN), to decode gaze information present in eye images and achieved significantly simplified gaze estimation. In this paper, a gaze estimation method that utilizes a CNN for gaze estimation that can be applied to various platforms without additional hardware is presented. An easy and fast data collection method is used for collecting face and eyes images from an unmodified desktop camera. The proposed method registered good results; it proves that it is possible to predict the gaze with reasonable accuracy without any additional tools.