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In our work, we focus on the non-intrusive methods. In the non-intrusive methods, a common strategy is to use the depth information or the image information to isolate the hand from the background [ 7 ]. After the hand isolating segmentation process, a classification process is performed [ 8 ]. There are several works in the field that apply these two steps to recognize hand gestures using depth information [ 9 , 10 , 11 , 12 ] and using the image information [ 13 ].
In these methods, if the segmentation process exhibits a poor performance, the recognition system decreases its accuracy dramatically. Some depth sensors are not designed for outdoor applications, and the use of these devices requires an external power source. In the same way, the computer vision methods that use the image information to perform the hand segmentation have problems with different lighting conditions or complex scenarios. Other types of approaches to recognize hand gestures using computer vision-based methods are reviewed by Murthy and Jadon [ 14 ].
In recent works, the hand recognition systems proposed by Pisharady et al. According to Rautaray and Agrawal [ 17 ], achieving performance near to real time is an important feature for hand recognition systems.
However, there is no standard hand posture dataset to perform a fair comparison between different methods. The lack of a standard hand posture dataset implies that there is no common methodology to perform the evaluation of the system. To avoid the problems with hand segmentation and the time needed to process an image, the object detection methods based on boosting have proven to exhibit a good compromise between accuracy and time.
The object detection framework first proposed by Viola and Jones [ 18 ] is an example of such a compromise. The Viola—Jones framework has been the base for several approaches to detect hand postures [ 19 , 20 , 21 ] and hand gestures [ 3 ]. In all of these works, the authors use the same image dataset.
One problem with the Viola—Jones framework is the low accuracy to detect objects with mobile parts, for example pedestrians. Several works in the pedestrian detection area have been proposed [ 23 ], but these approaches need extra hardware GPU to be executed in real time.
From this review, we observe the need for hand recognition systems capable of recognizing the hand gestures in the least possible time. Using the RTDD as the core detector, we train a set of detectors specialized in a single hand posture and use them jointly to perform multiclass hand posture detection.
The parallel operation of the detectors is accelerated by sharing the computation of features among all of the sets of RTDDs. Furthermore, we observe the lack of a standard dataset and a standard evaluation protocol. A good evaluation protocol gives meaningful information about the performance of the system.
This paper is organized as follows. First, we review the hand posture detection methods based on boosting, the available hand posture datasets and the evaluation methodology in Section 2. Our proposed methodology is described in Section 3 , where we describe the RTDD and the modifications proposed to perform multiclass detection. The results obtained using the proposed methodology are shown in Section 4. In Section 5 , the conclusions of this work are given.
Hand Gesture Recognition Many methods have been proposed to solve the hand gesture recognition problem. Hand gesture recognition is divided into three main phases: detection, tracking and recognition. For an extensive revision of the proposed methods in the literature, the reader can review [ 4 ].
In our review, we focus only on the hand detection step. First, we describe the methodologies to perform the hand gesture recognition using computer vision and how the performance evaluation is carried out.
We also review the main issues of the learning process to obtain a good classifier.