Image Recognition Market worth $98.6 billion by 2029, Growing At a CAGR of 16.1% – Report by MarketsandMarkets™

Chicago, May 17, 2024 (GLOBE NEWSWIRE) — The global Image Recognition Market, is expected to grow from USD 46.7 billion by 2024 to USD 98.6 billion by 2029 at a Compound Annual Growth Rate (CAGR) of 16.1% during the forecast period, according to a new report by MarketsandMarkets™. The proliferation of smartphones equipped with high-quality cameras has led to a significant increase in the generation and consumption of digital images and videos, which is expected to drive the growth of the global image recognition market.

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Image Recognition Market Dynamics:


  1. Increasing need for image recognition in automotive industry
  2. Rising demand for security applications
  3. Increasing use of high-bandwidth data services
  4. Technological advancements among CPG and retail companies


  1. High development cost of image recognition
  2. Data privacy and regulatory concerns


  1. Integration of AI capabilities with image recognition solutions
  2. Increase in demand for big data analytics

List of Key Players in Image Recognition Market:

  • Google (US)
  • Qualcomm (US)
  • AWS (US)
  •  Microsoft (US)
  •  Toshiba (Japan)
  •  NVIDIA (US)
  •  Oracle (US)
  •  NEC (Japan)
  •  Huawei (China)
  •  Hitachi (Japan)
  •  Forcepoint (US)

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Based on the offering, the software segment will grow at the highest CAGR during the forecast period. Image recognition software is an application of artificial intelligence and computer vision technologies that enable computers to analyze, interpret, and understand visual data such as images and videos. These software solutions are designed to replicate the human ability to recognize objects, scenes, patterns, and even faces within digital imagery. Image recognition software typically employs complex algorithms and deep learning models trained on vast datasets to extract meaningful features from visual inputs and make accurate predictions or classifications. Image recognition software is also used in security and surveillance systems for facial recognition, object detection, and anomaly detection, enhancing safety measures in public spaces and critical infrastructure. Furthermore, image recognition software enables machines to intelligently perceive and respond to their environments in autonomous vehicles, industrial automation, and robotics.

Based on the technology, facial recognition will grow at the highest CAGR during the forecast period.

Facial recognition technology utilizes algorithms that analyze facial characteristics such as the distance between the eyes, the nose’s shape, and the face’s contours to create a unique biometric profile for each individual. Facial recognition algorithms can compare these biometric profiles against stored templates in a database or live video feeds in real-time to accurately match faces and identify individuals. Facial recognition technology finds applications in various sectors, including security, law enforcement, retail, banking, and hospitality. Security and surveillance systems enhance access control measures by allowing organizations to authenticate individuals based on facial biometrics, thereby improving security and preventing unauthorized access to sensitive areas. Additionally, facial recognition technology enables personalized customer experiences in retail environments, which can be used for customer identification, targeted marketing, and customized recommendations based on previous interactions. Despite its potential benefits, facial recognition technology raises concerns regarding privacy, surveillance, and possible misuse, prompting ongoing discussions about ethical and regulatory considerations surrounding its deployment.

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Based on the vertical, BFSI vertical will hold the largest market share in 2024. Image recognition technology is utilized in the BFSI sector for various purposes, including identity verification, fraud detection, and customer service optimization. By employing advanced facial recognition algorithms, financial institutions can verify customers’ identities remotely, reducing the need for in-person visits and enhancing the overall user experience. Additionally, image recognition aids in detecting fraudulent activities by analyzing patterns and anomalies in transactions, thereby safeguarding the integrity of financial systems. Moreover, image recognition is integrated into automated teller machines (ATMs) and mobile banking applications to facilitate check deposits and authentication processes; this expedites transactions, reduces manual errors, and improves operational efficiency. Overall, image recognition technology is a vital tool for bolstering security protocols and enhancing the efficiency of banking and financial services within the BFSI sector.

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