Gesture recognition method and gesture recognition device

A gesture recognition method and a gesture recognition device are provided. The gesture recognition method includes the steps of: obtaining a hand image including a gesture graphic; determining a reference point in the gesture graphic; determining circular arc reference lines by using the reference point as a center; determining intersection points of each of the circular arc reference lines intersecting with a boundary of the gesture graphic; determining whether at least two finger blocks of a plurality of finger blocks of the gesture graphic conform to an approaching trend according to the circular arc reference lines and the intersection points, and determining whether the at least two finger blocks in a selected range of the gesture graphic forms a continuous graphic block; and when the at least two finger blocks of the gesture graphic conform to the approaching trend and form the continuous graphic block, determining the hand image as a hand pinch image.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of Taiwan application serial no. 108107629, filed on Mar. 7, 2019. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification.

BACKGROUND

Technical Field

The disclosure relates to a recognition technique, and more particularly, to a gesture recognition method and a gesture recognition device.

Description of Related Art

In general, the early human-machine interaction functions of various head-mounted displays (HMD) for virtual reality (VR)/augmented reality (AR) could not enter the general consumer market due to the limitations of the computing processing speed, the big and heavy device body, the lack of supporting application software, and the high price. However, in recent years, electronic hardware technology has greatly improved, the hardware computing processing capability has been significantly enhanced, and the development of application software has also increased. Therefore, there has been a boom in application designs related to HMD devices in recent years. Moreover, with the popularization of mobile devices, product development of HMD devices mostly moves toward designs that are compact and lightweight but meanwhile have high processing power.

To enable the HMD device to provide a simulated interactive experience with the virtual environment, the HMD device is generally equipped with a sensor or a lens to capture and determine the user's gesture. In this regard, in the human-machine interaction functions of the HMD device, how to effectively determine the gesture/action of the user in an accurate and rapid manner has always been one of the important issues in the field. For example, in the case of pinch gesture recognition, the image analysis method of a general HMD device involves capturing the entire image including the hand image of the user in front of the lens and then performing image recognition processing on the entire image to determine whether the hand is in a pinch gesture and subsequently perform other corresponding software interaction functions. However, with the computing resources being limited by the physical size and the costs, the HMD device may not be able to support such a large data computation amount or may not be able to provide the recognition function in real-time. In view of the above, to realize accurate gesture recognition effect in real-time without consuming a lot of computing resources, the disclosure will provide a solution of at least one embodiment in the description below.

SUMMARY

In view of the above, the disclosure provides a gesture recognition method and a gesture recognition device that can effectively analyze a hand image of a user to accurately recognize whether the user's gesture is a pinch gesture.

A gesture recognition method according to an embodiment of the disclosure includes the following steps. A hand image is obtained, wherein the hand image includes a gesture graphic. A reference point in the gesture graphic is determined. A plurality of circular arc reference lines are determined by using the reference point as a center. A plurality of intersection points of each of the circular arc reference lines intersecting with a boundary of the gesture graphic are determined. It is determined whether at least two finger blocks of a plurality of finger blocks of the gesture graphic conform to an approaching trend according to the plurality of circular arc reference lines and the plurality of intersection points, and it is determined whether the at least two finger blocks in a selected range of the gesture graphic form a continuous graphic block. When the at least two finger blocks of the gesture graphic conform to the approaching trend and form the continuous graphic block, the hand image is determined to be a hand pinch image.

A gesture recognition device according to an embodiment of the disclosure includes an image capturing device and a processor. The image capturing device is configured to obtain a hand image. The hand image includes a gesture graphic. The processor is electrically coupled to the image capturing device. The processor is configured to analyze the gesture graphic of the hand image to determine a reference point in the gesture graphic. The processor determines a plurality of circular arc reference lines by using the reference point as a center, and determines a plurality of intersection points of each of the circular arc reference lines intersecting with a boundary of the gesture graphic. The processor determines whether at least two finger blocks of a plurality of finger blocks of the gesture graphic conform to an approaching trend according to the plurality of circular arc reference lines and the plurality of intersection points, and determines whether the at least two finger blocks in a selected range of the gesture graphic form a continuous graphic block. When the at least two finger blocks of the gesture graphic conform to the approaching trend and form the continuous graphic block, the processor determines that the hand image is a hand pinch image.

Based on the above, the gesture recognition method and the gesture recognition device of the disclosure can analyze the hand image of the user in real-time to first determine whether the plurality of finger blocks of the gesture graphic in the hand image conform to the approaching trend, and then determine whether the finger blocks form a continuous graphic block, so as to determine whether the hand image of the user is a hand pinch image. Therefore, the gesture recognition method and the gesture recognition device of the disclosure can accurately recognize whether the user's gesture is a pinch gesture.

DESCRIPTION OF THE EMBODIMENTS

To make the content of the disclosure more comprehensible, at least one embodiment will be provided below as an example for implementing the disclosure accordingly. In addition, wherever possible, elements, components, and steps labeled with the same numerals in the drawings and the embodiments represent the same or similar parts.

FIG. 1is a schematic view of a gesture recognition device according to an embodiment of the disclosure. Referring toFIG. 1, a gesture recognition device100includes a processor110, an image capturing device120, a storage130, and a gesture recognition program131that is stored in the storage130and may be loaded and executed by the processor110. The processor110is electrically coupled to the image capturing device120. In the present embodiment, the gesture recognition device100is, for example, applicable to various electronic devices or multimedia devices to provide a real-time gesture recognition function. Particularly, in specific embodiments, the gesture recognition device100of the disclosure may be combined with or applied to a head-mounted display (HMD) for virtual reality (VR) or augmented reality (AR), but the disclosure is not limited thereto. The gesture recognition device100of the present embodiment may provide a gesture recognition function and provide a gesture recognition result to a backend application or a backend device to perform other corresponding functions or operations.

Specifically, when a user's hand is located in front of the image capturing device120, and the gesture recognition device100is performing the gesture recognition function, the image capturing device120obtains a hand image of the user, and the hand image includes a gesture graphic. Next, the image capturing device120provides the hand image to the gesture recognition program131to cause the processor110to perform image analysis. It is noted that the gesture recognition device100of the present embodiment is configured to provide a function of effectively determining whether a gesture of the user is a pinch gesture (e.g., an action in which the end of the user's thumb is in contact with the end of the index finger, and the rest of the fingers are folded against the palm; however, the disclosure is not limited thereto, and the end of the thumb may be in contact with the end of other one or more fingers). In other words, the processor110determines whether the hand image is a hand pinch image. In addition, in the present embodiment, the hand image analyzed by the processor110may be a grayscale or grayscale-processed image which further undergoes binarization processing, and the region in the gesture graphic and the region outside the gesture graphic in the hand image have different pixel values.

In the present embodiment, the processor110may be a graphics processing unit (GPU), an image signal processor (ISP), a central processing unit (CPU), another programmable general-purpose or specific-purpose microprocessor, digital signal processor (DSP), programmable controller, application specific integrated circuit (ASIC), programmable logic device (PLD), another similar processor, or a combination of the above processor circuits. Moreover, the gesture recognition device100of the present embodiment may further include a memory140. The memory140may be configured to store image data141obtained by the image capturing device120and the image data141that is temporarily stored during processing of the gesture recognition program.

In the present embodiment, the image capturing device120may be a depth camera, an infrared camera, or an RGB camera. Taking the depth camera as an example, when the user's hand is located in front of the depth camera, the depth camera may obtain a hand image of the user based on the closer foreground through a determination of the distance threshold value. Taking the infrared camera as an example, when the user's hand is located in front of the infrared camera, the infrared camera may obtain the hand image by determining the region having the highest reflection brightness. Taking the RGB camera as an example, when the user's hand is located in front of the infrared camera, the RGB camera may find a region that is most similar to a hand through a pre-designed training model or a relevant learning algorithm to obtain the hand image.

Further, in an embodiment, if the image capturing device120is a depth camera or an RGB camera, the processor110first performs image data simplification on the hand image provided by the image capturing device120. In other words, the processor110may first perform binarization processing on the hand image to facilitate subsequent image analysis. However, in another embodiment, if the image capturing device120is an infrared camera, since the image data provided by the infrared camera is already a grayscale image, the processor110may first perform fast binarization processing on the grayscale hand image, and then perform subsequent image analysis. In even another embodiment, if the hand image provided by the infrared camera is already a binary black-and-white image, the processor110may directly analyze the hand image provided by the infrared camera, and the processor110is not required to additionally perform binarization processing.

FIG. 2is a flowchart of hand image analysis according to an embodiment of the disclosure. Referring toFIG. 1andFIG. 2, after being loaded, the gesture recognition program131of the gesture recognition device100may perform steps S210to S290ofFIG. 2to realize the gesture recognition function. In step S210, the gesture recognition device100obtains a hand image through the image capturing device120and provides the hand image to the processor110to cause the processor110to analyze the hand image. It is noted that the hand image analyzed by the processor110is a binarized image. However, whether processor110performs binarization processing on the hand image depends on the type of the image capturing device120. In step S220, the processor110analyzes the hand image to determine a reference point in a gesture graphic of the hand image. In step S230, the processor110determines a plurality of circular arc reference lines by using the reference point as a center. In step S240, the processor110determines a number of fingers according to the plurality of circular arc reference lines. In step S250, the processor110determines a finger trend of the gesture graphic according to the plurality of circular arc reference lines. In step S260, the processor110determines whether a trend in which at least two fingers are approaching each other is present in the finger trend. If negative, the processor110performs step S210again to obtain a next hand image. If affirmative, the processor110performs step S270to recognize a number of finger blocks of the gesture graphic.

In step S270, the processor110recognizes a number of finger blocks of the gesture graphic according to the plurality of circular arc reference lines. In step S280, the processor110determines whether the number of the finger blocks of the gesture graphic is one. If negative, the processor110performs step S210again to obtain a next hand image. If affirmative, the processor110performs step S290to output a gesture recognition result as a pinch gesture. In other words, in the present embodiment, the gesture recognition device100performs recognition in two stages. In the first stage, the processor110first analyzes whether a trend in which at least two fingers are approaching each other is present in the finger trend of the gesture graphic to indicate that the user's gesture may be a pinch gesture. If the gesture graphic in the hand image satisfies the above condition in the first stage, the processor110performs the second stage. In the second stage, the processor110further analyzes the number of the finger blocks of the gesture graphic. If the number of the finger blocks of the gesture graphic is one, it means that a plurality of finger blocks are connected to each other to form a continuous graphic block, and that the user's finger action is a pinch action. Conversely, if the finger blocks of the gesture graphic is not a continuous graphic block, it means that the user's finger action is not a pinch action. Accordingly, the gesture recognition device100of the present embodiment can provide an accurate real-time pinch gesture recognition result.

To allow those skilled in the art to further understand the implementation details of the gesture recognition of the disclosure, two different examples of the hand image as presented inFIG. 3andFIG. 4will be described in detail below.

FIG. 3is a schematic view of analysis on a hand image according to an embodiment of the disclosure. Reference is made toFIG. 1toFIG. 3as well as the flowchart of the gesture recognition method ofFIG. 2. In step S210(corresponding to an analysis stage P1), the gesture recognition device100obtains a hand image310through the image capturing device120and provides the hand image310to the processor110to cause the processor110to analyze a gesture graphic311in the hand image310. A pixel value in the region of the gesture graphic311in the binarized hand image310is, for example, 255, and a pixel value outside the region of the gesture graphic311is, for example, 0, but the disclosure is not limited thereto. In step S220(corresponding to an analysis stage P2), the processor110calculates an average of a plurality of coordinate values of the gesture graphic311to determine a reference point321in the gesture graphic311of the hand image310. In other words, the processor110uses the graphic center point of the gesture graphic311as the reference point321. In step S230(corresponding to analysis stages P3to P5), the processor110determines a farthest point331corresponding to the reference point321in the gesture graphic311, and the processor110determines a plurality of radii having different lengths corresponding to a plurality of circular arc reference lines351_1to351_10based on a connecting line (having a length of r, for example) between the reference point321and the farthest point331. In the present embodiment, the plurality of circular arc reference lines351_1to351_10may respectively be semi-circular arc lines, and the plurality of circular arc reference lines are spaced apart at an equal interval. The radii of the plurality of circular arc reference lines351_1to351_10may be as shown in Table 1 below. However, the number and the interval of the circular arc reference lines of the disclosure are not limited to those shown in Table 1 below. In an embodiment, the number and the interval of the circular arc reference lines may be correspondingly designed according to different gesture recognition requirements.

TABLE 1Reference lineRadiusCircular arc reference line 351_1r/10Circular arc reference line 351_22r/10Circular arc reference line 351_33r/10Circular arc reference line 351_44r/10Circular arc reference line 351_55r/10Circular arc reference line 351_66r/10Circular arc reference line 351_77r/10Circular arc reference line 351_88r/10Circular arc reference line 351_99r/10Circular arc reference line 351_10r

In step S240(corresponding to an analysis stage P6), the processor110determines an intersection point number of a plurality of intersection points of each of the plurality of circular arc reference lines351_1to351_10intersecting with the boundary of the gesture graphic311in a counterclockwise manner, for example. In the present embodiment, the boundary of the gesture graphic311refers to a borderline of a pixel value change, for example, from black (the pixel value of the region outside the gesture graphic is 0) to white (the pixel value of the region in the gesture graphic is 255), or from white to black. Therefore, the intersection point number of the plurality of intersection points of each of the plurality of circular arc reference lines351_1to351_10intersecting with the boundary of the gesture graphic311may be as shown in Table 2 below. In the present embodiment, the processor110may select one of the plurality of circular arc reference lines351_1to351_10that has the highest intersection point number with respect to the boundary of the gesture graphic311to determine the number of fingers. Moreover, taking Table 2 as an example, the processor110may determine that the circular arc reference line351_6has the most intersection points with the boundary of the gesture graphic311. For example, three intersection points361,363, and365changing from black to white and three intersection points362,364, and366changing from white to black are present between the circular arc reference line351_6and the boundary of the gesture graphic311. Therefore, the processor110may determine that the number of fingers of the gesture graphic311is three based on the plurality of intersection points361to366of the circular arc reference line351_6. However, in other embodiments, if the plurality of circular arc reference lines351_1to351_10include multiple circular arc reference lines equally having the most intersection points, the processor110selects one that is farthest from the reference point321as the basis for determining the number of fingers.

In step S250(corresponding to an analysis stage P7), the processor110selects the circular arc reference line351_6, which has the highest intersection point number, as a first recognition boundary, and selects the circular arc reference line351_9, which is the previous circular arc reference line to the circular arc reference line351_10farthest from the reference point, as a second recognition boundary. In the present embodiment, from all the intersection points of each of the circular arc reference lines between the first recognition boundary and the second recognition boundary, the processor110selects coordinates of two intersection points that are adjacent to each other and are located in the gesture graphic, and calculates their center point coordinates to be defined as a finger skeleton point. Therefore, the plurality of circular arc reference lines351_6to351_9generate a plurality of finger skeleton points B1to B7. In other words, the processor110is only required to analyze a portion of the hand image310. Next, according to a plurality of skeleton point connecting lines of the plurality of finger skeleton points B1to B7, the processor110determines whether the plurality of finger blocks of the gesture graphic311conform to a trend in which at least two skeleton point connecting lines are approaching each other. The approaching trend means, for example, that the shape of one finger block is approaching the shape of another finger block, but the disclosure is not limited thereto. However, the determination of the trend analysis (e.g., divergence or convergence of multiple data over time) is a conventional technical means commonly used in statistical analysis of various engineering or financial data. The trend analysis functions are also provided in general spreadsheet software and conventionally publicly known techniques, which shall not be repeatedly described herein. In the present embodiment, the processor110respectively connects the plurality of finger skeleton points B1to B7in each of the finger blocks to determine the plurality of skeleton point connecting lines. For example, the skeleton points B1to B4form a connecting line, and the skeleton points B5to B7form another connecting line.

In step S260(corresponding to the analysis stage P7), when the plurality of skeleton point connecting lines include at least two skeleton point connecting lines that are approaching each other, the processor110determines that the plurality of finger blocks of the gesture graphic311conform to the approaching trend, and the processor110performs step S270. Conversely, when none of the plurality of skeleton point connecting lines are approaching each other, the processor110determines that the plurality of finger blocks of the gesture graphic311do not conform to the approaching trend, and the processor110performs step S210again to obtain a next hand image.

In step S270(corresponding to analysis stages P8to P9), the processor110selects the circular arc reference line351_7, which is the next circular arc reference line to the circular arc reference line351_6having the highest intersection point number, as a third recognition boundary, and selects the circular arc reference line351_10, which is farthest from the reference point321, as a fourth recognition boundary. In step S280, the processor110determines whether the plurality of finger blocks of the gesture graphic311between the third recognition boundary and the fourth recognition boundary is connected into one. In this regard, as shown by a partial hand image320that is cut out between the third recognition boundary and the fourth recognition boundary, since the plurality of finger blocks of the gesture graphic311that are cut out between the third recognition boundary and the fourth recognition boundary are not connected into one (there are two blocks in the partial hand image320), the plurality of finger blocks of the gesture graphic311between the third recognition boundary and the fourth recognition boundary do not form a continuous graphic block. Therefore, the processor110determines that the gesture graphic311of the hand image310is not a pinch gesture (as, in general, the end of the thumb and the end of another finger in the pinch gesture are brought into contact with each other and connected together), and the processor110performs step S210again to obtain a next hand image. Accordingly, by analyzing a portion of the hand image310(it is only required to analyze the image content between the two recognition boundaries), the gesture recognition device100of the present embodiment can accurately recognize in real-time that the gesture graphic311of the hand image310is not a pinch gesture, and it is not required to continuously compute or process the entire hand image310.

FIG. 4is a schematic view of analysis on another hand image according to an embodiment of the disclosure. Reference is made toFIG. 1,FIG. 2, andFIG. 4as well as the flowchart of the gesture recognition method ofFIG. 2. In step S210(corresponding to an analysis stage P1′), the gesture recognition device100obtains a hand image410through the image capturing device120and provides the hand image410to the processor110to cause the processor110to analyze a gesture graphic411in the hand image410. A pixel value in the region of the gesture graphic411in the hand image410is, for example, 255, and a pixel value outside the region of the gesture graphic411is, for example, 0, but the disclosure is not limited thereto. In step S220(corresponding to an analysis stage P2′), the processor110calculates an average of a plurality of coordinate values of the gesture graphic411to determine a reference point421in the gesture graphic411of the hand image410. In other words, the processor110uses the graphic center point of the gesture graphic411as the reference point421. In step S230(corresponding to analysis stages P3′ to P5′), the processor110determines a farthest point431corresponding to the reference point421in the gesture graphic311, and the processor110determines a plurality of radii having different lengths corresponding to a plurality of circular arc reference lines451_1to451_10based on a connecting line (having a length of r, for example) between the reference point421and the farthest point431. In the present embodiment, the plurality of circular arc reference lines451_1to451_10may respectively be semi-circular arc lines, and the plurality of circular arc reference lines are spaced apart at an equal interval. The radii of the plurality of circular arc reference lines451_1to451_10may be as shown in Table 3 below. However, the number and the interval of the circular arc reference lines of the disclosure are not limited to those shown in Table 3 below. In an embodiment, the number and the interval of the circular arc reference lines may be correspondingly designed according to different gesture recognition requirements.

TABLE 3Reference lineRadiusCircular arc reference line 451_1r/10Circular arc reference line 451_22r/10Circular arc reference line 451_33r/10Circular arc reference line 451_44r/10Circular arc reference line 451_55r/10Circular arc reference line 451_66r/10Circular arc reference line 451_77r/10Circular arc reference line 451_88r/10Circular arc reference line 451_99r/10Circular arc reference line 451_10r

In step S240(corresponding to an analysis stage P6′), the processor110determines an intersection point number of a plurality of intersection points of each of the plurality of circular arc reference lines451_1to451_10intersecting with the boundary of the gesture graphic411in a counterclockwise manner, for example. In the present embodiment, the boundary of the gesture graphic411refers to a borderline of a pixel value change, for example, from black (the pixel value of the region outside the gesture graphic is 0) to white (the pixel value of the region in the gesture graphic is 255), or from white to black. Therefore, the intersection point number of the plurality of intersection points of each of the plurality of circular arc reference lines451_1to451_10intersecting with the boundary of the gesture graphic411may be as shown in Table 4 below. In the present embodiment, the processor110may select one of the plurality of circular arc reference lines451_1to451_10that has the highest intersection point number with respect to the boundary of the gesture graphic411to determine the number of fingers. Moreover, taking Table 4 as an example, the processor110may determine that the circular arc reference line451_7has the most intersection points with the boundary of the gesture graphic411. For example, three intersection points461,463, and465changing from black to white and three intersection points462,464, and466changing from white to black are present between the circular arc reference line451_7and the boundary of the gesture graphic411. Therefore, the processor110may determine that the number of fingers of the gesture graphic411is three based on the plurality of intersection points461to466of the circular arc reference line451_7. However, in other embodiments, if the plurality of circular arc reference lines451_1to451_10include multiple circular arc reference lines equally having the most intersection points, the processor110selects one that is farthest from the reference point421as the basis for determining the number of fingers.

In step S250(corresponding to an analysis stage P7′), the processor110selects the circular arc reference line451_7, which has the highest intersection point number, as a first recognition boundary, and selects the circular arc reference line451_9, which is the previous circular arc reference line to the circular arc reference line451_10farthest from the reference point, as a second recognition boundary. In the present embodiment, from all the intersection points of each of the circular arc reference lines between the first recognition boundary and the second recognition boundary, the processor110selects coordinates of two intersection points that are adjacent to each other and are located in the gesture graphic, and calculates their center point coordinates to be defined as a finger skeleton point. Therefore, the plurality of circular arc reference lines451_7to451_9generate a plurality of finger skeleton points C1to C6. In other words, the processor110is only required to analyze a portion of the hand image410. Next, according to a plurality of skeleton point connecting lines of the plurality of finger skeleton points C1to C6, the processor110determines whether the plurality of finger blocks of the gesture graphic411conform to a trend in which at least two skeleton point connecting lines are approaching each other. In the present embodiment, the approaching trend means, for example, that the shape of one finger block is approaching the shape of another finger block, but the disclosure is not limited thereto. In the present embodiment, the processor110respectively connects the plurality of finger skeleton points C1to C6in each of the finger blocks to determine the plurality of skeleton point connecting lines. For example, the skeleton point C1is a single point, the skeleton points C2to C4form a connecting line, and the skeleton points C5to C6form another connecting line.

In step S260(corresponding to the analysis stage P7′), when the plurality of skeleton point connecting lines include at least two skeleton point connecting lines that are approaching each other, the processor110determines that the plurality of finger blocks of the gesture graphic411conform to the approaching trend, and the processor110performs step S270. Conversely, when none of the plurality of skeleton point connecting lines are approaching each other, the processor110determines that the plurality of finger blocks of the gesture graphic411do not conform to the approaching trend, and the processor110performs step S210again to obtain a next hand image.

In step S270(corresponding to analysis stages P8′ to P9′), the processor110selects the circular arc reference line451_8, which is the next circular arc reference line to the circular arc reference line451_7having the highest intersection point number, as a third recognition boundary, and selects the circular arc reference line451_10, which is farthest from the reference point421, as a fourth recognition boundary. In step S280, the processor110determines whether the plurality of finger blocks of the gesture graphic411between the third recognition boundary and the fourth recognition boundary is connected into one. In this regard, as shown by a partial hand image420that is cut out between the third recognition boundary and the fourth recognition boundary, since the plurality of finger blocks of the gesture graphic411that are cut out between the third recognition boundary and the fourth recognition boundary are connected into one, the plurality of finger blocks of the gesture graphic411between the third recognition boundary and the fourth recognition boundary form a continuous graphic block. Therefore, the processor110determines that the gesture graphic411of the hand image410is a pinch gesture. The processor110performs step S290to output a gesture recognition result for the backend application or the backend device to perform other corresponding functions or operations. Accordingly, by analyzing a portion of the hand image410(it is only required to analyze the image content between the two recognition boundaries), the gesture recognition device100of the present embodiment can accurately recognize in real-time that the gesture graphic411of the hand image410is a pinch gesture, and it is not required to continuously compute or process the entire hand image410.

FIG. 5is a flowchart of a gesture recognition method according to an embodiment of the disclosure. Referring toFIG. 1andFIG. 5, the gesture recognition method of the present embodiment is applicable to at least the gesture recognition device100of the embodiment ofFIG. 1to cause the gesture recognition device100to perform steps S510to S560. In step S510, the gesture recognition device100obtains a hand image through the image capturing device120. In step S520, the gesture recognition device100analyzes the hand image through the processor110to determine a reference point in a gesture graphic of the hand image. In step S530, the processor110determines a plurality of circular arc reference lines by using the reference point as a center. In step S540, the processor110determines a plurality of intersection points of each of the plurality of circular arc reference lines intersecting with a boundary of the gesture graphic. In step S550, the processor110determines whether a plurality of finger blocks of the gesture graphic conform to an approaching trend according to the plurality of circular arc reference lines and the plurality of intersection points, so as to further determine whether the plurality of finger blocks of the gesture graphic form a continuous graphic block. In step S560, when the plurality of finger blocks of the gesture graphic conform to the approaching trend and form the continuous graphic block, the processor110determines that the hand image is a hand pinch image. Therefore, according to the gesture recognition method of the present embodiment, the gesture recognition device100can accurately recognize the user's gesture in real-time.

In addition, regarding the implementation details of the steps of the present embodiment and other implementation features of the gesture recognition device100, reference may be made to the contents of the foregoing embodiments ofFIG. 1toFIG. 4to obtain sufficient teachings, suggestions, and implementation descriptions, which shall not be repeatedly described herein.

In summary of the above, the gesture recognition method and the gesture recognition device of the disclosure can analyze the hand image of the user in real-time to first determine whether the plurality of finger blocks of the gesture graphic in the partial hand image conform to the approaching trend, and then determine whether the finger blocks in the partial hand image form a continuous graphic block, so as to determine whether the hand image of the user is a hand pinch image. Therefore, the gesture recognition method and the gesture recognition device of the disclosure can realize the gesture recognition function without continuously computing the entire hand image data.