SBIR Phase I: Face Analyzer / Semantic Search

SBIR 第一阶段：人脸分析器/语义搜索

• 批准号: 2335287
• 负责人: Taleb Alashkar
• 金额: $ 27.5万
• 依托单位: ALGOFACE INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335287&HistoricalAwards=false
• 关键词: SBIR; Phase; Face; Analyzer; Semantic

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is significant as the company’s advanced Face Analysis AI technology will accelerate AI projects by 18-24 months. This innovative technology is poised to have a positive influence on various sectors such as retail and public safety, offering applications that go beyond facial recognition/identification. By focusing on collaborative human-AI facial tracking and analysis, this technology addresses ethical concerns and mitigates the risks and consequences of traditional facial recognition technologies. The project promotes diversity and inclusion in STEM fields by emphasizing a diverse team to combat bias and equity issues in technology development. The technology can contribute to national defense efforts by enabling efficient search for facial attributes of interest based on semantic queries. This aspect is particularly relevant in public safety scenarios with large crowds and high-security concerns. By reducing bias, improving accuracy, and addressing privacy and ethical concerns, the technology can have a lasting impact on the AI industry while advancing the welfare of the American public and supporting security efforts.This Small Business Innovation Research (SBIR) Phase I project aims to create Face Analyzer/Semantic Search, an AI system bridging descriptive text and facial photos. Unlike conventional face recognition systems, which necessitate a probe photo for comparisons, the company's innovation seeks to eliminate this requirement. This approach offers benefits in terms of time, cost, and accuracy, challenging the conventional wisdom in the field. The project's initial challenge involves assembling diverse training datasets with labeled face photos and textual descriptions, establishing a scalable data pipeline to enhance accuracy and mitigate bias. The second challenge is assessing the accuracy of facial attribute classification models derived from text and images across various attributes, image types, sizes, and ambient conditions. The third challenge involves optimizing model size and computational efficiency for cost-effective deployment. The proposed solution entails constructing a comprehensive training image dataset, expanding computer vision capabilities, developing a natural language processing module, and implementing a matching system. Key milestones for product development include creating precise facial image indexing modules, enabling the extraction of facial attributes from textual descriptions, and efficiently deploying the system in the cloud. The innovation promises to streamline and enhance facial analysis, potentially reshaping the field of face AI.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业创新研究(SBIR)第一阶段项目的更广泛/商业影响是显著的，因为该公司先进的人脸分析AI技术将使AI项目加速18-24个月。这项创新技术将对零售和公共安全等各个领域产生积极影响，提供超越面部识别/识别的应用。通过专注于协作的人类-AI面部跟踪和分析，这项技术解决了伦理问题，并缓解了传统面部识别技术的风险和后果。该项目通过强调一个多样化的团队来打击技术开发中的偏见和公平问题，促进了STEM领域的多样性和包容性。这项技术可以通过基于语义查询高效搜索感兴趣的面部属性，为国防工作做出贡献。这一方面在人群众多和高度安全的公共安全场景中尤其相关。通过减少偏见，提高准确性，解决隐私和伦理问题，这项技术可以对人工智能行业产生持久的影响，同时提高美国公众的福利，并支持安全努力。这个小企业创新研究(SBIR)第一阶段项目旨在创建面部分析器/语义搜索，这是一个连接描述性文本和面部照片的人工智能系统。与传统的人脸识别系统不同，该公司的创新试图消除这一要求。传统人脸识别系统需要探测照片进行比较。这种方法在时间、成本和准确性方面都有好处，挑战了该领域的传统智慧。该项目最初的挑战包括将带有标签的人脸照片和文字描述的不同训练数据集组合在一起，建立一个可扩展的数据管道以提高准确性和减轻偏见。第二个挑战是评估从不同属性、图像类型、大小和环境条件下的文本和图像派生的面部属性分类模型的准确性。第三个挑战涉及优化模型大小和计算效率，以实现经济高效的部署。提出的解决方案需要构建一个全面的训练图像数据集，扩展计算机视觉能力，开发自然语言处理模块，并实现匹配系统。产品开发的关键里程碑包括创建精确的面部图像索引模块，实现从文本描述提取面部属性，以及在云中高效部署该系统。这项创新承诺简化和增强面部分析，可能重塑人脸人工智能领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。