DIMENSIÓN ECONÓMICA

From the hand posture captured, the useful hand features are extracted and classified to achieve hand posture recognition. Different hand feature extraction and classification techniques are available as discussed in the following.

2.5.1 Hand Posture Feature Extraction

A wide range of hand features has been derived from hand configuration data for hand posture recognition [92-94]. Lengths and widths of fingers, palm and hand have been considered in some research [95-97]. For example, in [95], it uses the lengths of finger and palm to correct the hand posture classification error, and in [97], it uses the lengths of finger, palm and hand from an obtained hand gesture image for recognition of digits made in sign languages. Although different attempts have been made to adopt the length and width measurements as hand gesture recognition features, they have been proved to be more suitable for the personal identification applications [98, 99].

Some research tried to use the fingertip positions as features for hand posture recognition. In [100], Hsiao et al. argues that the positions of fingertips and the centre of the palm are the most important hand posture features. They proposed an algorithm to estimate the position of each fingertip with respect to palm and to recognise hand postures based on finger-palm distance vectors. However, it seems that the posture recognition based on fingertip positions is more applicable for object manipulation and machine operation rather than for the communication purpose, such as, sign languages [101-104].

On the other hand, the hand joints angles and the angles between two fingers are more suitable for recognition of hand postures in sign languages [105-108]. A typical example is the DigitEyes hand tracking system, which considered the finger phalange lengths and joint angles as features for hand modelling [109]. The advantage of using the joint angle features is that it is more invariant to the size and position of the hand [94].

Apart from these, most research works choose to consider the hand holistically, which use the appearance of the hand, such as the shape and contour, as features for hand posture recognition [110, 111]. The general concept of such approach is to find the best match between the input hand shape and the hand shape templates. The difficulty associated with this approach comes from the large variance of the hand sizes and orientations.

2.5.2 Hand Posture Feature Classification

Based on the literature review, there are three commonly used feature classification methods for hand posture recognition, Fuzzy systems, HMM networks and Neural Networks.

The Fuzzy theory was introduced by Zadeh in 1965 [112]. Since it deals with the ambiguous inputs adaptively and produces approximate results rather than exact ones, it has been applied to the usually imprecise hand features [113]. Early attempts on the use of fuzzy systems for hand posture recognition were reported by Holden et al., where fuzzy expert systems were used to classify finger bending into three states, slightly bent, greatly bent and completely closed, for recognition of hand gestures in Australasian sign language [114, 115]. The system evaluation was conducted based on a dictionary of 21 signs and the system performance was shown to be significantly better than the system without the fuzzy method. In [116], Su presented a fuzzy rule-based approach for spatio-temporal hand gesture recognition. For each posture input, it is tested by the fuzzy rules prior to the comparison with the hand posture templates, where the templates are selected based on the hyper-rectangular composite neural networks. The system has been tested against a database with 90 sign words consisting of 34 basic hand shapes as shown in Fig. 2-5, and the author declared a recognition rate of 94.1%. Another example is Bedregal’s hand gesture recognition system for Brazilian Sign Language, where the interval fuzzy rule based method is used to classify the hand joint data acquired from the data gloves [117]. Through these literatures, it can be seen that the Fuzzy system may be considered as a possible approach with good performance for hand posture recognition,

because it is capable to deal with the uncertainty in the hand measurement parameters.

Other feature classification methods for hand posture recognition are largely based on the HMM and Neural Networks, which matches the extracted features with the pre-modelled feature templates [92, 93, 118, 119]. Since these techniques are also applied widely for movement classification, they will be discussed together in the section of movement classification.

2.6 HAND MOVEMENT TRACKING

For hand movement tracking, it can also be done by vision based or sensor based approaches.

2.6.1 Vision Based Hand Movement Tracking

For the vision based hand movement tracking approach, video cameras are often used to capture a sequence of images containing hand movements over a period of time, and the hand trajectory is retrieved according to the hand positions obtained from each image. This approach can also be further divided into appearance based and model based methods.

A. Appearance based movement tracking

For the appearance based movement tracking methods, the derivation of an object movement trajectory in a scene is through the analysis of the motion of the features or

brightness patterns associated with the object in the image sequence [120, 121]. The

simplest approach is to track the movement of the segmented object centre. A more sophisticated approach was proposed by Bobick and Davis. It computes the motion energy images through a series of low resolution images, wherein it tracks the centres of the human’s body parts and results in the spatial movement information of the body parts to be obtained [122]. This is then followed by matching the movement trajectory against stored action energy image models to recognise the movement. In some other articles of Bobick and Davis, they generated a binary motion-energy image to represent the place where the motion occurred, and a motion-history image that is a grey-scale image with the intensity proportional to the recentness of the movement. The human movement is determined through the comparison with the predefined temporal templates [123, 124]. The result showed that the system worked at a low speed of 9 Hz and failed when two people presented in one scene due to the occlusion from each other. Also, it cannot deal with non-specified body parts. In [125], Kolsh and Truk used a set of KLT (stands for the initials of the authors) feature trackers to track the motion of a hand. The features are taken from the bounded skin colour blob of the hand and its centre position is determined by the median feature. The system has achieved a detection rate of 92.23%. In [126], a