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Fig. 18 Acquisition speed of the entire experiment for each user no: 301 Pattern Processing Lab”. Twenty-five volunteers participated in these experiments. The volunteers chose characters using the virtual keyboard and entered the appropriate characters as input. The gestural flick input system is shown in Fig. 15. Inputting the desired characters, the user performed the ‘close’ gesture via the first character of the flick character sequences on the PVK. The system then displayed the secondary virtual keyboard. The user then selected the appropriate character for input by performing the ‘close’ gesture. The selected characters were input, and the system again showed the primary virtual keyboard. Figure 16

Figure 18 Acquisition speed of the entire experiment for each user no: 301 Pattern Processing Lab”. Twenty-five volunteers participated in these experiments. The volunteers chose characters using the virtual keyboard and entered the appropriate characters as input. The gestural flick input system is shown in Fig. 15. Inputting the desired characters, the user performed the ‘close’ gesture via the first character of the flick character sequences on the PVK. The system then displayed the secondary virtual keyboard. The user then selected the appropriate character for input by performing the ‘close’ gesture. The selected characters were input, and the system again showed the primary virtual keyboard. Figure 16

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shown in Fig. 3a—c, respectively. Certain special characters and operators are also included in the English and numerical virtual keyboards. Table 1 presents the flick character sequences of the virtual keyboard. Finally, the user performs the character input by following this flick character sequence. For example, if a user wants to enter a character ‘f’, he or she moves to English PVK. Then select the first character in the sequence of the corresponding character, that is ‘e’ and serform the gesture functions. The system displays the SVK as in Fig. 3e. The user then selects the appropriate character ‘f? and performs a gesture function in the corresponding block to input the sharacter.
shown in Fig. 3a—c, respectively. Certain special characters and operators are also included in the English and numerical virtual keyboards. Table 1 presents the flick character sequences of the virtual keyboard. Finally, the user performs the character input by following this flick character sequence. For example, if a user wants to enter a character ‘f’, he or she moves to English PVK. Then select the first character in the sequence of the corresponding character, that is ‘e’ and serform the gesture functions. The system displays the SVK as in Fig. 3e. The user then selects the appropriate character ‘f? and performs a gesture function in the corresponding block to input the sharacter.
Fig. 2 A flick keyboard used in smartphones: a Japanese, b English
Fig. 2 A flick keyboard used in smartphones: a Japanese, b English
Fig. 3 Proposed primary virtual keyboard a Japanese, b English, and ¢ Numeric; d flick input; e secondary virtual keyboard The system captures user gestures via an interface with a webcam. The input to the system is an image frame of a static hand gesture from live video. To resolve the unwanted area of the video frame to detect the hand gesture, a specific area(s) of the virtual keyboard is imposed as the ROI containing only a hand gesture image. In order to increase our ability to detect the
Fig. 3 Proposed primary virtual keyboard a Japanese, b English, and ¢ Numeric; d flick input; e secondary virtual keyboard The system captures user gestures via an interface with a webcam. The input to the system is an image frame of a static hand gesture from live video. To resolve the unwanted area of the video frame to detect the hand gesture, a specific area(s) of the virtual keyboard is imposed as the ROI containing only a hand gesture image. In order to increase our ability to detect the
Table 1 Flick character sequences of the virtual keyboard hand, we identify those pixels that match the color of the hand in a particular frame and perform color segmentation. In this segmentation process, we convert the RGB color space and use HSV color model analysis by thresholding the saturation (S) and value (V) elements. In HSV representation, the hue, saturation, and values determine the color of the hand, the intensity of the color and the lightness of the image, respectively. A Gaussian blurring technique is used in the input to smooth the image and reduce the noise. We also use threshold masking for image segmentation and apply multiple masks for color segmentation. Low and high threshold masks are used for hue, saturation, and value in which the pixels are set to one, and the remaining pixels to zero. We use different thresholds for different areas of the same image, so that it is well segmented in the hand area under different forms of illumination. Threshold values are fixed according to various tests depending on the training dataset. The minimum (S/,,,) and maximum (Sj,g;) limits of saturation are 0.01 and 1, respectively, while the values of Vj, and Vpjjg, are 0 and 1, respectively. When the threshold is not applicable, the hue components of Ajo and hjjg, are 0 and 1, respectively. The system identifies the color of the hand if Eq. (1) is satisfied:
Table 1 Flick character sequences of the virtual keyboard hand, we identify those pixels that match the color of the hand in a particular frame and perform color segmentation. In this segmentation process, we convert the RGB color space and use HSV color model analysis by thresholding the saturation (S) and value (V) elements. In HSV representation, the hue, saturation, and values determine the color of the hand, the intensity of the color and the lightness of the image, respectively. A Gaussian blurring technique is used in the input to smooth the image and reduce the noise. We also use threshold masking for image segmentation and apply multiple masks for color segmentation. Low and high threshold masks are used for hue, saturation, and value in which the pixels are set to one, and the remaining pixels to zero. We use different thresholds for different areas of the same image, so that it is well segmented in the hand area under different forms of illumination. Threshold values are fixed according to various tests depending on the training dataset. The minimum (S/,,,) and maximum (Sj,g;) limits of saturation are 0.01 and 1, respectively, while the values of Vj, and Vpjjg, are 0 and 1, respectively. When the threshold is not applicable, the hue components of Ajo and hjjg, are 0 and 1, respectively. The system identifies the color of the hand if Eq. (1) is satisfied:
Fig. 4 Color analysis of a hand gesture image: a input, blurred and processed image (top to bottom, respective- ly); b, e HSV distribution of the color of the hand and histogram of the input image (top to bottom, respectively)
Fig. 4 Color analysis of a hand gesture image: a input, blurred and processed image (top to bottom, respective- ly); b, e HSV distribution of the color of the hand and histogram of the input image (top to bottom, respectively)
Fig. 5 The CNN model for feature extraction and includes five convolution levels and three fully connected network layers [36]. AlexNe measures the simplicity and expertise in a shallow model combined with a deep model predictior of performance. It has numerous filters per layer, and deep with stacked convolutional level. Ths convolutional level is to detect the input local features and move the convolution through the fixec length of the kernel steps. Since the convolution kernel is fixed, but the weight parameters are obtained through the running layer by layer. Figure 5 shows the architecture of the feature extraction model. The input levels are defined in the first layer, and the size of the input imag« is 128 x 128 x 3. The first convolutional layer filters the input images with a 4-pixel stride with 9¢ kernels of 11x11x3 size. The output of the first convolutional layer acts as the input to the seconc convolutional layer and filters it with a 256 kernel of 5x5x256 size. The others convolutiona
Fig. 5 The CNN model for feature extraction and includes five convolution levels and three fully connected network layers [36]. AlexNe measures the simplicity and expertise in a shallow model combined with a deep model predictior of performance. It has numerous filters per layer, and deep with stacked convolutional level. Ths convolutional level is to detect the input local features and move the convolution through the fixec length of the kernel steps. Since the convolution kernel is fixed, but the weight parameters are obtained through the running layer by layer. Figure 5 shows the architecture of the feature extraction model. The input levels are defined in the first layer, and the size of the input imag« is 128 x 128 x 3. The first convolutional layer filters the input images with a 4-pixel stride with 9¢ kernels of 11x11x3 size. The output of the first convolutional layer acts as the input to the seconc convolutional layer and filters it with a 256 kernel of 5x5x256 size. The others convolutiona
Fig. 6 OVM-MCSVM structure for identifying gesture functions
Fig. 6 OVM-MCSVM structure for identifying gesture functions
Table 2 Description of different gesture functions
Table 2 Description of different gesture functions
Fig. 8 Examples of the hand gesture dataset images
Fig. 8 Examples of the hand gesture dataset images
Fig. 9 Examples of the hand gesture processed images The user also performs various hand gestures as gesture functions in the character input system. The user executes the gesture functions via the non-character block; these gesture functions are described in Table 2. The user is able to enter a new line by holding up the index finger, and can add a space by holding up two fingers (index and middle finger). When three fingers (index, middle and ring finger) are held up, the system deletes a previously input character. The backspace is executed when four fingers are displayed. The system detects an
Fig. 9 Examples of the hand gesture processed images The user also performs various hand gestures as gesture functions in the character input system. The user executes the gesture functions via the non-character block; these gesture functions are described in Table 2. The user is able to enter a new line by holding up the index finger, and can add a space by holding up two fingers (index and middle finger). When three fingers (index, middle and ring finger) are held up, the system deletes a previously input character. The backspace is executed when four fingers are displayed. The system detects an
Fig. 11 Hand gesture recognition model: a model accuracy, and b model losses
Fig. 11 Hand gesture recognition model: a model accuracy, and b model losses
Table 3. Average recognition accuracy of different gesture functions for each subject
Table 3. Average recognition accuracy of different gesture functions for each subject
Table 4 Results of hand gesture recognition
Table 4 Results of hand gesture recognition
Fig. 12 Confusion matrix of hand gesture recognition accuracy
Fig. 12 Confusion matrix of hand gesture recognition accuracy
Fig. 13 Comparison the accuracy of recognizing different gesture functions of the proposed and state-of-ar method and 20% for testing. A total of 10,080 x 4096 features were extracted for training and 2520 x 4096 for testing. These features help in the classification of different hand gesture functions. Figure 10 illustrates a method of hand gesture recognition that is based on a supervised learning technique. In this method, the input images of typical hand gestures are pre- processed and segmented. In the training process, the system is pre-trained based on the feature gestures obtained using CNN. The extracted features are used to train the SVM. In the recognition process, the features are extracted from the user’s new gesture and classified by trained SVM. Pre-training process enhances model performance and recognition accuracy.
Fig. 13 Comparison the accuracy of recognizing different gesture functions of the proposed and state-of-ar method and 20% for testing. A total of 10,080 x 4096 features were extracted for training and 2520 x 4096 for testing. These features help in the classification of different hand gesture functions. Figure 10 illustrates a method of hand gesture recognition that is based on a supervised learning technique. In this method, the input images of typical hand gestures are pre- processed and segmented. In the training process, the system is pre-trained based on the feature gestures obtained using CNN. The extracted features are used to train the SVM. In the recognition process, the features are extracted from the user’s new gesture and classified by trained SVM. Pre-training process enhances model performance and recognition accuracy.
Table 5 Comparison of recognition accuracy with state-of-the-art algorithms (Evaluated using our dataset)
Table 5 Comparison of recognition accuracy with state-of-the-art algorithms (Evaluated using our dataset)
Fig. 14 Comparison the accuracy of recognizing different gesture functions for each user of the proposed and state-of-art method
Fig. 14 Comparison the accuracy of recognizing different gesture functions for each user of the proposed and state-of-art method
Fig. 15 Procedure used in the gestural flick input system
Fig. 15 Procedure used in the gestural flick input system
Fig. 16 Example of PVK and SVK display screens
Fig. 16 Example of PVK and SVK display screens
Fig. 17 Character input simulation using the virtual keyboard
Fig. 17 Character input simulation using the virtual keyboard
Table 6 Average recognition accuracy of character input and input performance
Table 6 Average recognition accuracy of character input and input performance
Fig. 19 Average character recognition accuracy of all users shows the virtual keyboard screen displays of different character sets. To capture the gesture, the system uses a maximum position of one second; if the system does not receive any gesture activity within the ROI within one second, the gesture is not accepted. If the user fails to perform the gesture within the suggested time, the system cancels the gesture at this time and waits for the next gesture. The ‘open’ gesture acts as a no gesture activity, however, is used for the hand movement for character selection and input performance. Figure 17 depicts an example of the character input system. The character input speed and recognition accuracy of the entire experiment for each user are shown in Figs. 18 and 19, respectively, and the average recognition accuracy of character input and the speed of input are given in Table 6. The character recognition rate of this system was 100%, although there were some errors in selecting the character based on gesture functions in this system. One reason for incorrect character recognition was that the user performed the gesture functions near to the character borders. Despite this, the proposed system achieved a 2.07% error rate for character selection, as shown in Table 5. The overall accuracy of character recognition was 97.93%. Moreover, the average speed of the character input was 29.0 [characters/min], and the speed of character input using our system is therefore satisfactory. The time performances of the character inputs compared with the state-of-the-art systems are presented in Table 7. The recognition accuracy compared to some previous studies as accuracy of character input and different gesture function recognition are given in Table 8.
Fig. 19 Average character recognition accuracy of all users shows the virtual keyboard screen displays of different character sets. To capture the gesture, the system uses a maximum position of one second; if the system does not receive any gesture activity within the ROI within one second, the gesture is not accepted. If the user fails to perform the gesture within the suggested time, the system cancels the gesture at this time and waits for the next gesture. The ‘open’ gesture acts as a no gesture activity, however, is used for the hand movement for character selection and input performance. Figure 17 depicts an example of the character input system. The character input speed and recognition accuracy of the entire experiment for each user are shown in Figs. 18 and 19, respectively, and the average recognition accuracy of character input and the speed of input are given in Table 6. The character recognition rate of this system was 100%, although there were some errors in selecting the character based on gesture functions in this system. One reason for incorrect character recognition was that the user performed the gesture functions near to the character borders. Despite this, the proposed system achieved a 2.07% error rate for character selection, as shown in Table 5. The overall accuracy of character recognition was 97.93%. Moreover, the average speed of the character input was 29.0 [characters/min], and the speed of character input using our system is therefore satisfactory. The time performances of the character inputs compared with the state-of-the-art systems are presented in Table 7. The recognition accuracy compared to some previous studies as accuracy of character input and different gesture function recognition are given in Table 8.
Table 7 Comparison of character input time with state-of-the-art systems webcam to 33.33 ms per frame. Figure 20 shows the average recognition accuracies for the different gestures, and Fig. 21 presents the average recognition accuracy for different users and different gestures. From these results, we can see that the ‘open’ and ‘close’ gestures achieved higher recognition accuracy than the other gestures. However, the average recognition rate of the gesture functions in real time for this technique was 96.23%.
Table 7 Comparison of character input time with state-of-the-art systems webcam to 33.33 ms per frame. Figure 20 shows the average recognition accuracies for the different gestures, and Fig. 21 presents the average recognition accuracy for different users and different gestures. From these results, we can see that the ‘open’ and ‘close’ gestures achieved higher recognition accuracy than the other gestures. However, the average recognition rate of the gesture functions in real time for this technique was 96.23%.
Table 8 Recognition accuracy comparison with other related studies
Table 8 Recognition accuracy comparison with other related studies
Fig. 20 Average recognition accuracy of different gesture functions
Fig. 20 Average recognition accuracy of different gesture functions
Fig. 21 Average accuracy of different gesture functions for each user VEAL CRA Oe FV COS ESI Ree SRD, ede TM: RANT Se ee eee Based on the obtained results, the recognition accuracy of character input is 97.93% shown ir Table 6. The results indicate that the performance of the proposed system was robust among user: although different users showed variability in writing speed. The average speed of writing characters is 114 s shown in Table 6. An example of character writing speed among the users is illustrated in Fig. 18. Despite the inherent variability among users, the non-touch character writing system achieves a maximum accuracy of 100% and a minimum of 95% is shown in Fig. 19. The reason for the minimum accuracy is that the user selects the character near the character boundary which produces the incorrect character. Moreover, we compared the time performances and the recognition accuracy of character input is shown in Tables 7 and 8, respectively. The evaluatior results confirm that this system achieves higher recognition accuracy than conventional one. Compared to the character input, the user performs a character in 2.08 s with a 2.07% error rate is
Fig. 21 Average accuracy of different gesture functions for each user VEAL CRA Oe FV COS ESI Ree SRD, ede TM: RANT Se ee eee Based on the obtained results, the recognition accuracy of character input is 97.93% shown ir Table 6. The results indicate that the performance of the proposed system was robust among user: although different users showed variability in writing speed. The average speed of writing characters is 114 s shown in Table 6. An example of character writing speed among the users is illustrated in Fig. 18. Despite the inherent variability among users, the non-touch character writing system achieves a maximum accuracy of 100% and a minimum of 95% is shown in Fig. 19. The reason for the minimum accuracy is that the user selects the character near the character boundary which produces the incorrect character. Moreover, we compared the time performances and the recognition accuracy of character input is shown in Tables 7 and 8, respectively. The evaluatior results confirm that this system achieves higher recognition accuracy than conventional one. Compared to the character input, the user performs a character in 2.08 s with a 2.07% error rate is
Md Abdur Rahim is currently working toward the Ph.D. degrees in the Pattern Processing Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Fukushima, Japan. He is working (now on study leave) as an assistant professor, Department of Computer Science and Engineering, Pabna University of Science and Technology, Bangladesh. He received the Bachelor of Science (Honours) and Master of Science (M.Sc.) degrees in Computer Science and Engineering from University of Rajshahi, Bangladesh in 2008 and 2009. His research interests include human computer interaction, pattern recognition, computer vision and image processing, human recognition and machine intelligence. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Md Abdur Rahim is currently working toward the Ph.D. degrees in the Pattern Processing Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Fukushima, Japan. He is working (now on study leave) as an assistant professor, Department of Computer Science and Engineering, Pabna University of Science and Technology, Bangladesh. He received the Bachelor of Science (Honours) and Master of Science (M.Sc.) degrees in Computer Science and Engineering from University of Rajshahi, Bangladesh in 2008 and 2009. His research interests include human computer interaction, pattern recognition, computer vision and image processing, human recognition and machine intelligence. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jungpil Shin received a Ph.D. in Communication Engineering from Kyushu University, Japan in 1999. He became an Associate Professor and Senior Associate Professor in the School of Computer Science and
Jungpil Shin received a Ph.D. in Communication Engineering from Kyushu University, Japan in 1999. He became an Associate Professor and Senior Associate Professor in the School of Computer Science and
Md. Rashedul Islam received the B.S. degree in computer science and engineering from the University Rajshahi, Rajshahi, Bangladesh, in 2006, the M.S. degree in informatics from the Hégskolan i Boras (University of Boras), Boras, Sweden, in 2011, and Ph.D. degree in electrical, electronic, and computer engineering at the University of Ulsan, Ulsan, South Korea, in 2016. He is currently working as a visiting researcher in University of Aizu, Japan also an Associate Professor (on study leave) in the Department of Computer Science and Engineering, University of Asia Pacific (UAP), Dhaka, Bangladesh. His research interests include Signal & Image processing, Machine learning, Feature selection and optimization, Human computer interaction, Data- driven bearing fault diagnosis and prognostics, and parallel algorithm/processing. He has several publications in major journals (SCI, SCIE) and conferences. He is reviewer of few SCIE journals. He has contributed to several conferences as organizing secretary, member of program committee, session chair and a reviewer. Engineering, the University of Aizu, Japan, in 1999 and 2004, respectively. His research interests include pattern recognition, character recognition, image processing, and computer vision. He is currently researching the following advanced fields: pen-based interaction system, real-time system, oriental character processing, mobile computing, computer education, human recognition, and machine intelligence. He served several conferences as a program chair and program committee member for numerous international conferences and serves as a reviewer for several SCI major journals and an editor of journals. He is a member of IEEE, ACM, IEICE, IPSJ, KISS, and KIPS.
Md. Rashedul Islam received the B.S. degree in computer science and engineering from the University Rajshahi, Rajshahi, Bangladesh, in 2006, the M.S. degree in informatics from the Hégskolan i Boras (University of Boras), Boras, Sweden, in 2011, and Ph.D. degree in electrical, electronic, and computer engineering at the University of Ulsan, Ulsan, South Korea, in 2016. He is currently working as a visiting researcher in University of Aizu, Japan also an Associate Professor (on study leave) in the Department of Computer Science and Engineering, University of Asia Pacific (UAP), Dhaka, Bangladesh. His research interests include Signal & Image processing, Machine learning, Feature selection and optimization, Human computer interaction, Data- driven bearing fault diagnosis and prognostics, and parallel algorithm/processing. He has several publications in major journals (SCI, SCIE) and conferences. He is reviewer of few SCIE journals. He has contributed to several conferences as organizing secretary, member of program committee, session chair and a reviewer. Engineering, the University of Aizu, Japan, in 1999 and 2004, respectively. His research interests include pattern recognition, character recognition, image processing, and computer vision. He is currently researching the following advanced fields: pen-based interaction system, real-time system, oriental character processing, mobile computing, computer education, human recognition, and machine intelligence. He served several conferences as a program chair and program committee member for numerous international conferences and serves as a reviewer for several SCI major journals and an editor of journals. He is a member of IEEE, ACM, IEICE, IPSJ, KISS, and KIPS.
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Information SystemsComputer ScienceArtificial IntelligenceHuman Computer InteractionGesture RecognitionVirtual Keyboard
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