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Development of benchmark datasets of multioriented hand gestures for speech and hearing disabled

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Abstract

Reliable hand gesture recognition is extremely relevant for automatic interpretation of sign languages used by people with hearing and speech disabilities. In this work, we present (i) new benchmark datasets of depth-sensor based, multi-oriented, isolated and static hand gestures of numerals and alphabets following the conventions of American Sign Language (ASL), (ii) an effective strategy for segmentation of hand region from depth data and appropriate preprocessing for feature extraction, and (iii) an effective statistical-geometrical feature set for recognition of multi-oriented hand gestures. Besides setting benchmark performances on the developed datasets, viz. 97.67%, 96.53% and 96.86% on numerals, alphabets and alpha-numerals respectively, the proposed pipeline is also implemented on two related public datasets and is found superior to state-of-the-art methods reported so far.

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Acknowledgements

This work is partially supported by three grants from the Govt. of India, namely, grant no. SR/WOS-A/ET-1001/2015, grant no. EMR/2016/007213 of the Department of Science and Technology (DST) within the Ministry of Science and Technology, and grant no. BT/PR16356/BID/7/596/2016 of the Department of Biotechnology and Rashtriya Uchchatar Shiksha Abhiyan (RUSA) from the Department of Higher Education, Govt. of India.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Jadavpur University, Kolkata, 700032, India

    Soumi Paul, Arpan Bhattacharyya, Mita Nasipuri & Subhadip Basu

  2. Université de Lorraine, LORIA, UMR 7503, Vandoeuvre-lès-Nancy, F-54506, France

    Hayat Nasser, Phuc Ngo & Isabelle Debled-Rennesson

  3. CNRS, LORIA, UMR 7503, Vandoeuvre-lès-Nancy, F-54506, France

    Hayat Nasser, Phuc Ngo & Isabelle Debled-Rennesson

  4. Department of Computer Science & Engineering, Aliah University, Kolkata, 700160, India

    Ayatullah Faruk Mollah

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  1. Soumi Paul
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Appendices

Appendix A: Theory of Discrete Contours

In this section, we recall a method of contour simplification based on selected dominant points. They are computed from a discrete structure, named adaptive tangential cover (ATC) reported by [21, 24], which is well adapted to analyze irregular noisy contours.

A.1 Adaptive Tangential Cover [21, 24]

An adaptive tangential cover (ATC) is composed of a sequence of maximal straight segments, called maximal blurred segments, of the studied contour. The

notion of maximal blurred segment has been introduced by [3] as an extension of arithmetical discrete line presented by [33] with a width parameter for noisy or disconnected digital contours.

Definition 1

An arithmetical discrete line \({\mathcal D}(a,b,\mu ,\omega )\), with a main vector (b,a), a lower bound μ and an arithmetic thickness ω (with \(a,b,\mu ,\omega \in \mathbb {Z}\) and gcd(a,b) = 1) is the set of integer points (x,y) verifying μ ≤ ax − by < μ + ω.

Definition 2

A set Sf is a blurred segment of width ν if the discrete line \({\mathcal D}(a,b,\mu ,\omega )\) containing Sf has the vertical (or horizontal) distance \(d=\frac {\omega -1}{\max \limits {(\mid a \mid , \mid b\mid )}}\) equal to the vertical (or horizontal) thickness of the convex hull of Sf, and d ≤ ν (see Fig. 9a).

Fig. 9
figure 9

(a) Example of arithmetical discrete line \({\mathcal D}(2 , -3 , -5 , 5)\) (grey and orange points) and a blurred segment of width ν = 1.4 (orange points) bounded by D. (b) Maximal blurred segment of width ν = 1.4 (orange points) - color figure online

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Let C be a discrete curve and Ci,j a sequence of points of C indexed from i to j. Let denote the predicate “Ci,j is a blurred segment of width ν” as BS(i,j,ν).

Definition 3

Ci,j is called a maximal blurred segment (MBS) of width ν and denoted MBS(i,j,ν) iff BS(i,j,ν), ¬BS(i,j + 1,ν) and ¬BS(i − 1,j,ν) (see Fig. 9b).

An ATC is designed to capture the local noise on curve by adjusting the thickness of maximal blurred segments in according with the amount of noise present along the contour. In order to prevent the local perturbation, the meaningful thickness [9, 10] estimator is integrated in the construction of ATC as a noise detector at each point of the curve. This meaningful thickness is used as an input parameter to compute the ATC with appropriate widths w.r.t. noise. A non-parametric algorithm is developed in [21, 24] to compute the ATC of a given discrete curve. In the ATC, the obtained MBS decomposition of various widths transmits the noise levels and the geometrical structure of the given discrete curve (see Fig. 10a, c).

Fig. 10
figure 10

(a, c): adaptive tangential cover, (b, d): polygonal representation (in red) using the dominant points and polygonal simplification results (in green) - color figure online

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A.2 Polygonal simplification [23,24,25]

Using the ATC, we detect the points of local maximum curvature, called dominant points (DP), on the digital curve. Such points contain a rich information which allows to characterize and describe the curve. Issued from the dominant point detection proposed in [23, 25] and the notion of ATC, an algorithm is developed in [24] to determine the dominant points of a given noisy digital curve C. The main idea is that the candidate dominant points are localized in the common zones of successive MBS of the ATC of C.

Then, by using a simple measure of angle m, we can identify the dominant point as point having the smallest angle. More precisely, this measure m is the angle between the considered point and the two left and right endpoints of the left and right MBS involved in the studied common zone. When the considered point varies, m becomes a function of it. A dominant point is defined as a local minimum of m. ATCs are illustrated in Fig. 10a, c, and Dominant points are illustrated in Fig. 10b, d in red points. Red lines represent the polygonal representation of the shape.

First goal of finding the dominant points is to have an approximate description of the input curve, called polygonal simplification. However, due to the nature of the tangential cover, dominant points usually stay very close to each others, which is presumably undesirable in particular for polygonal simplification. So, we associate to each detected dominant point a weight, i.e, the ratio of integral sum of square errors and the angle with the two dominant point neighbours, indicating its importance with respect to the approximating polygon of the curve. Polygonal simplification is illustrated in Fig. 10b, d with green lines.

B Database Images

B.1 Image Database JU_V2_DIGIT

We have collected 1000 images of 0 to 9 from 10 people. For each digit we have collected 10 orientations. To create this dataset, first we have created a small dataset collection tool. After setting up Kinect with the system, this tool helps us to save images on a single click of the save button.

As at runtime, we are able to locate the hand position, so on the click of the save button, we are able to save an RGB image of the whole scene, a depth image of the whole scene, an RGB image with hand region annotated in it, a cropped RGB image with only hand, cropped depth values of only hand, and hand and wrist depth values.

We use depth values here for our further experimentation. First, we convert our depth values to a depth image. Then we threshold the depth image to extract the Region of Interest and extracting the contours. Our further experimentation involves contour based feature extraction. Details are given in the main paper.

In Figs. 11, 12, 13, 14, 15, 16, 17, 18, 19 and 20, we present some sample images from this database. Image ID Pi_Gj_k means that it corresponds to the i-th person, the j-th gesture and the k-th orientation.

Fig. 11
figure 11

Image ID: P1_G0_3, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 12
figure 12

Image ID: P1_G1_4, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 13
figure 13

Image ID: P1_G2_5, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 14
figure 14

Image ID: P1_G3_4, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 15
figure 15

Image ID: P1_G4_10, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 16
figure 16

Image ID: P1_G5_1, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 17
figure 17

Image ID: P1_G6_3, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 18
figure 18

Image ID: P1_G7_6, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 19
figure 19

Image ID: P1_G8_6, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 20
figure 20

Image ID: P1_G9_10, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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B.2 Image Database JU_V2_ALPHA

We have collected another set of alphabetic images with the same tool mentioned above. Here we have captured from a to z (excluding j and z as they are dynamic) 24 static images from 10 people. Each image has 10 orientation. So all together there are 2400 images in the dataset.

In Fig. 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43 and Fig. 44, we present some sample images from this database. Image ID Pi_α_k means that it corresponds to the i-th person, the gesture α and the k-th orientation.

Fig. 21
figure 21

Image ID: P1_a_1, a RGB Full Image with Hand Annotation b Depth Full Scene Image c RGB Crop Image d Depth Crop Image e Depth Threshold Image f Contour Image of Hand

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Fig. 22
figure 22

Image ID: P1_b_6, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 23
figure 23

Image ID: P1_c_5, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 24
figure 24

Image ID: P1_d_3, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 25
figure 25

Image ID: P1_e_7, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 26
figure 26

Image ID: P1_f_9, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 27
figure 27

Image ID: P1_g_7, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 28
figure 28

Image ID: P1_h_4, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 29
figure 29

Image ID: P1_i_4, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 30
figure 30

Image ID: P1_k_8, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 31
figure 31

Image ID: P1_l_10, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 32
figure 32

Image ID: P1_m_2, a RGB Full Image with Hand Annotation b Depth Full Scene Image c RGB Crop Image d Depth Crop Image e Depth Threshold Image f Contour Image of Hand

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Fig. 33
figure 33

Image ID: P1_n_3, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 34
figure 34

Image ID: P1_o_2, a RGB Full Image with Hand Annotation b Depth Full Scene Image c RGB Crop Image d Depth Crop Image e Depth Threshold Image f Contour Image of Hand

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Fig. 35
figure 35

Image ID: P1_p_3, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 36
figure 36

Image ID: P1_q_4, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 37
figure 37

Image ID: P1_r_8, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 38
figure 38

Image ID: P1_s_4, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 39
figure 39

Image ID: P1_t_3, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 40
figure 40

Image ID: P1_u_10, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 41
figure 41

Image ID: P1_v_9, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 42
figure 42

Image ID: P1_w_10, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 43
figure 43

Image ID: P1_x_2, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

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Fig. 44
figure 44

Image ID: P1_y_2, (a) RGB Full Image with Hand Annotation (b) Depth Full Scene Image (c) RGB Crop Image (d) Depth Crop Image (e) Depth Threshold Image (f) Contour Image of Hand

Full size image

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Paul, S., Nasser, H., Mollah, A.F. et al. Development of benchmark datasets of multioriented hand gestures for speech and hearing disabled. Multimed Tools Appl 81, 7285–7321 (2022). https://doi.org/10.1007/s11042-021-11745-8

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